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12 July 2018

Margaret Gamalo, Qingzhao Yu, Sara Hughes - In this webinar we will hear why there is a case for Bayesian adaptive design in trials and learn how this can be done through an example. We will also hear why there is a need to do more research into paediatric drug evaluation and how extrapolation of adult clinical trials can help.

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19 June 2018

Ludwig Hothorn - Initially, two approaches for the dose-response analysis of toxicological bioassays are described in detail: a simultaneous testing of order-restricted multiple contrasts and regression-based modelling. Afterwards, the recent p-value controversy is discussed from the perspective of regulatory toxicology and finally, the question will be argued why the proof of safety is not widely used in routine up to now. All methods are demonstrated using //R-//CRAN packages.

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Dose-response analysis is a central part in statistical evaluation of toxicological bioassays. Two approaches are used: simultaneous testing of order-restricted multiple contrasts and regression-based modelling. The first one considers the DOSE qualitatively, i.e. as randomized factor whereas the second assumes DOSE as quantitative covariate (in bioassays commonly for grouped dose levels). Both approaches are demonstrated by means of real data examples where robustness, e.g. against downturn effects is discussed. Moreover, a new approach is explained, where DOSE is jointly considered both quali- and quantitatively. The recent p-value controversy is discussed from the perspective of regulatory toxicology where first confidence intervals for specific selected effect sizes are recommended. Secondly, the inclusion of individual data points within or without a prediction interval is proposed as an alternative to common-used null- hypothesis significance tests. The prediction intervals are defined for any single future value of a group with sample size n_i using the controls of multiple historical bioassays. The within- and between assay variance is considered by a mixed effect model. Finally, the question will be discussed why the proof of safety („be safe in negative results“) is not widely used in routine up to now. The “third main set” of statistics is: software must be available. And therefore all methods are demonstrated using //R-//CRAN packages.

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06 June 2018

Fiona Guillard - The TransCelerate consortium is an innovative and welcome initiative involving data-sharing of placebo data across multiple pharmaceutical companies, providing participating members with access to control arm data at the patient level: clearly a great help for designing studies in therapeutic areas where perhaps one’s own company’s experience is limited. This talk will demonstrate the data re-use platform being developed in GSK (the "R&D Information Platform – RDIP"), where all data available to GSK scientists will ultimately be stored, including the TransCelerate data. We will show how RDIP expedites the collation of relevant historical data, with an example based on the detection of Adverse Events in Schizophrenia clinical trials.

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From such data a meta-analytic prior is first derived, then mixed with
a vague alternative prior – which we call a “Cromwell” prior, for its ability to admit that informative historical data may turn out not to be relevant when confronted with data from a new randomised study. Bayesian dynamic borrowing methods are then used to develop and interrogate the potential designs of new clinical studies, with regard to their ability to identify key AEs with prespecified levels of confidence.

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06 June 2018

Ros Walley - Bayesian approaches are being increasingly employed in clinical study design, particularly in Early Development. For example, at UCB, we aim to conduct all our proof of concept studies within a Bayesian framework. Consequently, we anticipate an increase in clinical publications containing Bayesian analyses. In this talk we discuss the additional challenges of publishing results obtained from a Bayesian design. For example, whilst in any pharmaceutical company, the stakeholders who sign off on such clinical designs may all be familiar with and appreciate the Bayesian approach, there are additional stakeholders involved in getting a paper published. In particular, it may be difficult to persuade anonymous referees of the benefits of a Bayesian approach and of an appropriate way of describing such methods.

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Additional wordcount will need to be used to describe priors and the key success criteria. There may be a reluctance to publish any additional success criterion that relates primarily to internal decision-making. Methods using some form of adaptive downweighting e.g. robust priors to address potential prior-data conflict or mixture likelihoods to deal with outliers may be new to reviewers and viewed with some suspicion. In addition to outlining key issues for consideration, we will highlight useful literature – both guidelines and example publications - and discuss our own experience and learning.

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06 June 2018

Samuel Branders - The amount of data collected from patients involved in clinical trials is continuously growing. All those patient's characteristics are potential covariates that could be used to improve study analysis and power. At the same time, the development of computerized systems simplifies the access to huge amount of historical data. However, it is still difficult to leverage those big data when dealing with small clinical trials, such as in Phases I and II. Their restricted number of patients limits the possible number of covariates included in the analysis. The purpose of this talk is to present how machine learning can overcome this problem by taking advantage of historical data with larger sample sizes. We also put the approach in perspective with the regulatory guideline on the use of adjustment for baseline covariates.

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06 June 2018

Orlando Doehring - In the era of big data, there has been a surge in collected biomedical data, which has provided ample challenges for distributed computing but also posed novel inference questions. Classical machine learning techniques, such as logistic regression, neural networks, support vector machine and Gaussian processes performed very well in non-temporal prediction tasks but typically relied on the independence assumption. However, many recent application have longitudinal context in the form of short- and long-term dependencies. Hidden Markov Models proved popular to model longitudinal data but increasingly become less computationally feasible for a large number of hidden states. Recently, advances in parallel computing led to widespread use of deep learning approaches, such as recurrent neural networks and convolutional networks, and attracted attention due to their impressive results on sequence data. Finally, we will look in more detail at a case study from healthcare analytics which infers disease type from multiple irregularly sampled longitudinal observations, such as blood pressure, heart rate and blood oxygen saturation.

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06 June 2018

Dietmar Volz - In a subtyping approach, the supposedly continuous manifestation of Autism Spectrum Disorder (ASD) will be decomposed using clusters which are defined on the basis of clinical heterogeneity in subdomains covering e.g. repetitive behaviors, social interaction, cognition or communication ability. Approximately 2800 patients from the Simons Simplex Collection who have been diagnosed with ASD comprising subtypes of e.g. Idiopathic Autism or Aspergers and have valid scores on the ADI-R, SRS, RBS-R, and Vineland-II scales. To restrict the dimension of the dataspace, predominantly standardized composite scores will be used in lieu of item-level scores. Patients will be subjected to clustering applying Self-Organizing Maps (SOM) which is a well-established machine learning technique. Findings of SOM patient clustering will be discussed, and compared to age and IQ patterns. It will be shown that SOM is able to embed the term ‘correlation’ into a broader i.e. non-linear context.

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06 June 2018

Jixian Wang - Machine learning approaches have been increasingly used in subgroup identification to maximize patient’s benefits of treatments. They may also be used in treatment effect evaluation and adjustment for clinical trials and, especially, for observational studies in the big data environment. However, methods and applications in this area are less known to pharmaceutical statisticians. We give a short review on some recently developed approaches and report our recent research on double machine learning (DML).

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06 June 2018

Julien Tanniou, Anja Schiel, John Adler, Benjamin Hofner, Steven Teerenstra - The townhall was a place where you could ask burning questions and expect great responses from our panel of experts on any topics.

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06 June 2018

Oliver Keene, Stephen Ruberg, David Wright, Alexander Schacht - This presentation replicates the discussions which may go on during scientific advice. The example was in Respiratory. A patient representative (Stephen) kicked off the session, talking about what they wanted to be able to achieve and know about a new drug they may be prescribed. The Company (Oliver) went on to present their position and the estimand they were selecting. Then a Regulatory agency (David) and HTA (Alexander) gave feedback. A lively debate followed about the differing needs of Regulatory agencies vs HTA, with the patient voice often not taking centre stage.

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06 June 2018

Zachary Skrivanek - Visual Analytics combines automated analysis techniques with interactive data visualizations for an effective understanding, reasoning and decision-making. Visual Analytics is more than graphics, it is a process that optimizes your work flow, discharging cognitive and memorization burden to the visual cortex so the expert can focus on the important task: interpreting the data.

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Visual Analytics combines automated analysis techniques with interactive data visualizations for an effective understanding, reasoning and decision-making. Visual Analytics is more than graphics, it is a process that optimizes your work flow, discharging cognitive and memorization burden to the visual cortex so the expert can focus on the important task: interpreting the data.

There have been great advances in Visual Analytics, which the pharmaceutical industry has not exploited to the fullest extent yet. I will present some examples of where my organization has leveraged Visual Analytics to improve drug development in our company. As an example, Visual Analytics using appropriate idioms like a Sankey Diagram combined with proper cohort selection and data munging using machine learning techniques like similarity matrices can extract meaningful information from complex, noisy real world evidence data.

Another example is using Visual Analytics to assess spontaneous patient reported adverse events has reduced a tedious 2 week long process (formally based on reviewing Excel spread sheets) to less than a day. Visual Analytics has improved how we conduct Trial Level Safety Reviews (TLSR) to monitor the conduct and safety of a trial, eliminating the cost of producing statistical tables and more importantly improving the effectiveness of the TLSR. Visual Analytics can and should be used at every touch point where a user has to interpret data. Even a listing can be more effective using a simple interactive tool like a spreadsheet program where you can sort and filter versus a static listing. Visual Analytics can be used not only to improve your company’s transformation of data to information, but it can also be used to more effectively communicate results to regulatory agencies, thought leaders, payers and customers.

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06 June 2018

Nicole Mentenich, Bastian Becker and Christoph Tasto - Clinical trials generate vast information on adverse events, whose connections to each other can be complex and many-facetted. In order to show this complexity to its full extent, large numbers of tables are produced which often only show one aspect of the truth and are difficult to keep an overview of. In light of this abundance of data, graphical approaches increasingly gain importance. AdEPro – short for “Animation of Adverse Event Profiles” – is an innovative Shiny app to audio-visualize the safety profile of both the individual patient and the entire study cohort. AdEPro provides an impressive visual display of the distribution of adverse events and allows a thorough picture that considers temporal components and individual differences between patients. The app is not limited to a visual depiction, but even reinforces important events by means of a sound component, thus bringing safety profiles to life. In summary, AdEPro provides profound answers to complex questions surrounding adverse events and enables the user to come up with new hypotheses regarding the patients’ safety profile in a clinical trial.

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06 June 2018

Bodo Kirsch and Susanne Lippert - The Subgroup Explorer is a new application to analyze the population of clinical trials in minute detail. This allows the analysis of multiple subgroups to identify any groups that differed from the overall trial result. This reassures the clinical team that nothing has been overlooked, provides a deeper understanding of the investigated population, or leads to more targeted planning of future trials. And all this can be achieved within a single, comprehensive and coherent presentation. The Subgroup Explorer was developed by the Biostatistics Innovation Center at Bayer AG and is freely available.

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06 June 2018

Gaelle Saint-Hilary - The predictive probability of success of a future clinical trial is a key quantitative tool for decision-making in drug development. It is derived from prior knowledge and available evidence from the accumulated data, which in the current practice typically comes from previous clinical trials using the same clinical endpoint. However, a surrogate endpoint isoften used as primary endpoint in early development, while no or limited data are collected on the clinical endpoint of interest. We propose a general, reliable and reproducible methodology to predict the success of a future trial from surrogate endpoints, in a way that makes the best use of all the available evidence.

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The predictions are based on an informative prior, called surrogate prior, obtained from the data of past trials and external evidence on one or several surrogate endpoints. This prior could be combined with data on the clinical endpoint of interest, if available, in a classical Bayesian framework. Two methods are proposed to address a potential discordance between the surrogate prior and the data on the final endpoint. We investigated the patterns of behaviour of the predictions in a comprehensive simulation study, and we present an application to a drug development in Multiple Sclerosis. The proposed methodology is expected to support decision-making in many different situations, since the use of predictive markers is critical to accelerate drug developments and to select promising drug candidates, better and earlier.

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05 June 2018

Andrea Hita - The availability of high-dimensional data for use in pharmaceutical research and development has exploded in recent years. In this talk, Andrea Hita will overview a number of applications based on recent work including diagnostic development using biomedical signals and images, biomarker discovery, validation study design, and analysis of post marketing data.

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05 June 2018

Jaroslaw Harezlak - Accelerometers are frequently used to measure physical activity in large observational studies. Typical accelerometry data include activity counts or the step counts. However, such measures use only a fraction of the information contained in the raw accelerometry data. In my talk, I will overview the state-of-the-art methods and present our recent work on the classification of sitting and standing activities as well as on the classification of walking and stair-climbing. Issues discussed will include derivation of the meaningful features from the raw data (e.g. walking cadence), efficient computation on large datasets and association of physical activity measures with health outcomes.

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05 June 2018

Munshi Imran Hossain - Data obtained from clinical trials and healthcare can be of very high dimensions. The challenge is that the number of samples is usually a lot less compared to the number of features or the dimension of the data. In such a scenario, it is very important to choose the right subset of features to get predictive models with optimum performance. Genetic algorithm (GA) is a method of intelligently searching in this massive feature space. We give a brief introduction of the concept of GAs and then explain how it can be used for selection of features. We will also present some results obtained on using GAs for feature selection for a particular case study.

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05 June 2018

Ursula Garczarek - Pharmacovigilance (PhV) was defined by the WHO 1972 as the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other drug-related problem and was regarded as being synonymous with post-marketing surveillance for adverse drug reactions. Since this definition, the scope has been widened and means for data collection and data sources available for PhV have exploded. In parallel, the data science community (rather than the statistical community) has developed more and more tools to support PhV. I will try to give a broad overview of data science applications in the support for this research.

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05 June 2018

Orla Doyle - The increased volume and variety of healthcare data presents new opportunities for evidence generation and novel applications to address complex healthcare challenges. This presentation will discuss ways in which machine learning is increasingly being applied to find hard-to-find patients, such as undiagnosed patients with rare conditions. The presentation will illustrate the potential benefits of machine learning vs. classical statistical methods through case studies. Drawbacks to machine learning applications will also be highlighted, including a partial loss in transparency. The talk will describe factors behind the recent focus on artificial intelligence and comment on its potential role in patient-finding.

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05 June 2018

David Svensson - A key step in the evaluation of any pivotal Ph III RCT is to make a risk-benefit assessment and identify the right patient population to treat. One common approach is to conduct an interaction test per subgroup factor, or to perform a global interaction test before any further exploration of the results. There are well-known statistical issues with such approaches. In recent years, various alternative approaches have been proposed, all essentially based on avoiding isolated assessment of subgroup factors (one per time), and instead opt for a more holistic view, borrowing information across subgroups, aiming for some shrinkage of point estimates. The underlying thinking is that the strongest observed effects are overestimated, so some bias adjustment is motivated. We present methods proposed in the literature (including Bayesian shrinkage and Bootstrap-based bias reduction); we discuss their assumptions and their plausibility; we also illustrate their operating characteristics using simulated data.

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05 June 2018

Wouter Willems - In the Phase 3 development of a 3-DAA (direct-acting antiviral) combination for treatment of chronic hepatitis C, an innovative Bayesian design was proposed for establishing non-inferiority to standard of care (SOC). Combining direct evidence from the data in the current trial, with indirect information that is borrowed from historic data, allows to design more cost effective and efficient clinical studies. The augmentation of the SOC arm with historic efficacy information from publicly available clinical trials was proposed to efficiently use the available data for the SOC, as well as the information from treated patients in the new trial. Challenges were the selection of historical clinical trials, the proportion of borrowed data and the decision rule to declare non-inferiority that ensures type 1 error control. In the current trial, simulations were an integral part of the design development and justification to regulators. Positive feedback was received from the FDA.

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05 June 2018

Emily Graham - We are interested in the problem of decision-making for a portfolio of combination therapies. Several methods exist in the literature for portfolio planning which use stochastic programming techniques. However, the existing methods do not take into consideration the differences between single agent drug development and combination drug development. One of the key considerations to be made in a portfolio containing combinations is the potential to share information across studies of combinations that have at least one treatment in common. We consider a Bayesian framework where the success probability of a particular combination study is found using both historical data on the combination and information from related combination studies. This framework allows us to use all the available information regarding a combination, including emerging information, and feed this into the portfolio-level decisions that are made. We illustrate the developed methodology on sample portfolios and highlight the utility of incorporating related information.

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05 June 2018

Mouna Akacha - The primary interest in trials using recurrent event endpoints is usually to understand how treatment impacts the occurrence of recurrent events. For that we first have to decide how to measure the treatment effect under the repeated occurrence of an event, which in turn depends critically on the underlying scientific question. Depending on the specific setting, some estimands may be more appropriate than others. In this talk, we discuss the value and limitations of different treatment effect measures (estimands) and their associated statistical analyses (estimators) for recurrent event endpoints subject to terminal events. The estimands are further illustrated through a case study in chronic heart failure (CHF).

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05 June 2018

Antje Jahn - This work is motivated by clinical trials in chronic heart failure disease, where treatment/intervention has effects both on morbidity (assessed as recurrent non-fatal hospitalisations) and on mortality (assessed as cardiovascular death). A joint frailty proportional hazards model has been proposed for these kinds of outcomes. However, more often, marginal treatment effect estimates are presented as the main efficacy outcome. We investigate the consequences of applying such misspecified marginal models on treatment effect estimates. By the use of Laplace-transformations we derive the marginal hazard ratios as a function of time. We identify those parameters that cause a violation of the proportional hazards assumption and thus affect the bias and its degree in hazard ratio estimation. We show results on the direction and degree of bias for different situations and relate these results to published clinical trials.

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05 June 2018

Nan Van Geloven - Many papers have pointed out the important problem of competing risks in time-to-event analyses. In the presence of competing risks, the Kaplan Meier approach to calculating the cumulative probability of the event of interest has been repudiated and calculating cumulative incidences has become the standard. I argue that in certain competing risks situations, cumulative incidence may not necessarily be the quantity of interest. In particular when the competing event is not inevitable like death, but is a medical treatment or intervention the interest may lie in a different matter. I will discuss two applications, one from Nephrology and one from Reproductive Medicine, where cumulative incidence is not the way to go and present alternative analyses methods.

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05 June 2018

Pantelis Vlachos - Considerable interest has grown among pharmaceutical and other medical product developers in adaptive clinical trials, in which data collected during the course of a trial affects ongoing conduct or analysis of the trial. Following the release of the FDA draft Guidance document on adaptive design clinical trials in early 2010, expectations of an increase in regulatory submissions involving adaptive design features, particularly for confirmatory trials, were high. In this session we present a couple of case studies where adaptive designs were applied in a confirmatory setting as well as the statistical challenges resulting from the implementation of such designs. Recommendations will also be provided.

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05 June 2018

Deepak Parashar - There has been a surge in designing clinical trials based on the assumption that a biomarker is predictive of treatment response. Patients are stratified by their biomarker signature, and one tests the null hypothesis of no treatment effect in either the full population or the targeted subgroup. However, in order to verify the predictability of a biomarker, it is essential that hypothesis be tested in the non-targeted subgroup too.

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There has been a surge in designing clinical trials based on the assumption that a biomarker is predictive of treatment response. Patients are stratified by their biomarker signature, and one tests the null hypothesis of no treatment effect in either the full population or the targeted subgroup. However, in order to verify the predictability of a biomarker, it is essential that hypothesis be tested in the non-targeted subgroup too. In a Phase IIB oncology trial with progression free survival (PFS) endpoint, the data obtained can inform the Phase III design whether to restrict recruitment to just the targeted subgroup or not.

We propose a two-stage randomised Phase II population enrichment trial design, with PFS as the primary endpoint comparing an experimental drug with a control treatment. We adaptively test the null hypotheses of hazard ratios in both the targeted as well as the non-targeted subgroups, with strong control of the familywise error rate. It is assumed that the hazard ratio of the targeted subgroup is much less than that of non-targeted, since the drug is expected to be more beneficial for the biomarker-positive subpopulation.

Simulations for an example trial in non-small cell lung cancer show that the probability of recommending an enriched Phase III trial increases significantly with the hazard ratio in the non-targeted subgroup, and illustrate the efficiency achieved. Our adaptive design testing first in the non-targeted subgroup followed by testing in the targeted subgroup for a randomised controlled trial constitutes part of the proof of a biomarker’s predictability.

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05 June 2018

Thomas Burnett - We use a Bayesian decision framework to construct Adaptive Enrichment trials that choose the optimal trial population at an interim analysis. The possible decisions at the interim analysis must be pre-planned, for example continuing to recruit the same population as the first part of the trial, recruit only from a sub-population, or stopping the trial early for futility. We ensure strong control of the Familywise Error Rate using well known hypothesis testing methods. The properties of these tests are not impacted by using a Bayesian decision rule at the interim analysis, due to the pre-defined decisions.

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We use a Bayesian decision framework to construct Adaptive Enrichment trials that choose the optimal trial population at an interim analysis. The possible decisions at the interim analysis must be pre-planned, for example continuing to recruit the same population as the first part of the trial, recruit only from a sub-population, or stopping the trial early for futility.

We ensure strong control of the Familywise Error Rate using well known hypothesis testing methods. The properties of these tests are not impacted by using a Bayesian decision rule at the interim analysis, due to the pre-defined decisions.

These Adaptive Enrichment trials may be optimised to any given scenario, however this does not mean that an adaptive design is always the best choice. The Bayesian decision framework allows us to compare the effectiveness of Adaptive Enrichment and fixed sampling alternatives, showing which design is most suitable. We conduct simulation studies to demonstrate scenarios where Adaptive Enrichment trials can offer an improvement over fixed sampling designs.

These simulation studies make use of several simplifying assumptions, however the method of optimisation is very flexible. The key requirement is to evaluate the expected future behaviour of the trial. In practice this allows for trials that incorporate: multiple sub-populations (these must be pre-identified for the hypothesis testing), multiple interim analyses or the use of more complex data types such as survival endpoints or longitudinal observations.

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05 June 2018

Steven Ruberg - The world is exploding with data, and the application of ‘analytics’ is growing at a commensurate rate. The question remains: “Are we using the right data and are we applying smart analytics?” This talk will explore statistics versus data science and highlight how statisticians in the pharmaceutical industry can reinvent themselves to play a much broader role in their companies than the historical role in clinical development or R&D. It will also the value of statistics beyond data science, and encourage statisticians as well as our profession to insure we communicate our value appropriately.

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05 June 2018

Chiara Whichello - Background: To date, patients are not systematically consulted to provide their preferences during decision making processes along the medical product lifecycle. Recently, there has been a shift toward a more structured and quantitative approach with increasing consideration of the patient’s perspective on benefit-risk (B-R) tradeoffs. However, the extent to which patient preference data is valuable for medicines development and can be integrated into decision-making is not fully established. The Innovative Medicines Initiative (IMI) is a broad public-private partnership sponsored by the European Union and pharmaceutical industry association (EFPIA). With a consortium of academia, industry, HTA bodies and payers, regulators, patients, and patient groups, the IMI PREFER project aims to develop recommendations concerning how patient preferences can be assessed and used to inform medical product decision making. Objectives: To present the aims and methods of the PREFER project, in particular, its mission to integrate patient preferences into medical product decision making. Since PREFER is ongoing project, present progress will be presented.

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Background:

To date, patients are not systematically consulted to provide their preferences during decision making processes along the medical product lifecycle. Recently, there has been a shift toward a more structured and quantitative approach with increasing consideration of the patient’s perspective on benefit-risk (B-R) tradeoffs. However, the extent to which patient preference data is valuable for medicines development and can be integrated into decision-making is not fully established. The Innovative Medicines Initiative (IMI) is a broad public-private partnership sponsored by the European Union and pharmaceutical industry association (EFPIA). With a consortium of academia, industry, HTA bodies and payers, regulators, patients, and patient groups, the IMI PREFER project aims to develop recommendations concerning how patient preferences can be assessed and used to inform medical product decision making.

Objectives:

To present the aims and methods of the PREFER project, in particular, its mission to integrate patient preferences into medical product decision making. Since PREFER is ongoing project, present progress will be presented. 

Methods:

PREFER includes an examination of expectations, concerns, needs, and requirements related to the use of patient preference data. This was accomplished through literature reviews as well as semi-structured interviews and focus groups with stakeholders. Additionally, PREFER researchers identified and appraised different methods for eliciting and exploring patient preferences. The most appropriate methods will be used and tested during clinical case studies and simulations. The final PREFER deliverable is a set of recommendations on the assessment and use of patient-preference studies to inform regulatory, industry, and payer decision making.

Results:

We will present the PREFER work packages and describe how the project has gathered stakeholder input, translated input into research questions, and mapped research questions to methodologies. Additionally we will describe how the case studies will be executed and recommendations will be made on the use of patient preferences and elicitation methods. 

Conclusions:

The medical development environment is increasingly seeking the patient perspective in many aspects of trial design, conduct, and B-R assessment. The PREFER project addresses how to best incorporate patient preferences in order to inform medical product decision making by creating recommendations to inform regulatory, industry, and payer decision making.

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05 June 2018

Daniel Saure - When developing any product, it is crucial to understand the preferences of the customer with respect to different characteristics of the product in order to achieve success. This holds especially true in medical research where the optimal treatment should incorporate information on patient’s trade-offs e.g. of route of application vs efficacy or safety aspects. It is therefore important to try to understand the reasons behind patient decisions. Conjoint analysis is a well-known statistical method for investigating how consumers make trade-offs and choose between competing products or services. It can also be used to predict (simulate) consumer choices for future products or services. However, not only the preferences of the overall population are of interest, but to understand whether subgroups with specific preference profiles exists of if they depend on different patient characteristics. For example, preferences might depend on gender, age, or pre- treatment. Within our session we will show, how unsupervised learning approaches can be applied to patient’s preference data to identify specific subgroups based on a psoriasis case study. In this case study preferences were collected through a structured online survey for patients with Psoriasis. Hypothetical psoriasis treatments with the following conjoint attributes are investigated: dosage form, time to reach results, results skin, results itching, risk of impairing side effects, appearance of side effects. Subgroups are identified via cluster analyses as well as by latent class analyses. Strengths and limitations as well as practical problems will be discussed.

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05 June 2018

Necdet Gunsoy - Achieving reimbursement has become increasingly challenging due to resource limitations and the emergence of novel, often costly, medicines. At the same time, policy makers have encouraged patient involvement in clinical decision making. Embedding preference elicitation activities within drug development is becoming increasingly important for pharmaceutical companies to ensure that the right medicines are progressed and that the value of these new medicines are better represented. This talk presents the rationale and strategies for including preference elicitation activities within drug development, and will present case studies where drug development questions have been addressed using preference elicitation methods.

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05 June 2018

Janice Branson - When it comes to developing drugs, patients are at the heart of our business but also when it comes to sharing data, patients remain at the core. We aim to walk through the Novartis journey of Clinical Data Publication describing our initial approach, followed by details on how we performed a motivated intruder attack to get a better understanding of the risk of publically sharing data and finally describing our future outlook.

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05 June 2018

Ada Adriano - This talk provides an overview of the EMA Policy 0070, its objectives, the main features and achievements so far. It will highlight the key challenges faced in the anonymisation of personal data in the clinical reports, in particular risk of re-identification assessment, identification of plausible attackers and balance between protection of personal data and data utility. It will also be presented how the EMA Technical anonymisation group (TAG) will help further to develop best practices for the anonymisation of clinical reports.

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05 June 2018

Katherine Tucker & Chris Harbron - This talk provides a overview of the changing data sharing landscape, a framework for data anonymisation and practical considerations. It will include an brief outline of how data sharing has evolved over the past five years, voluntary sharing of patient-level data via routes such as 'CSDR' and registry, medical journal and regulatory requirements including EMA policy 0070. We provide an overview of a practical framework for data anonymisation, developed by the UK Anonymisation Network (UKAN), and consider how this can be applied in practice and integrated with established metrics for assessing anonymisation risk. We also consider the large scale behaviour of these metrics and their impact on the practicality of data sharing.

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05 June 2018

Hugo Pedder - Model-based meta-analysis (MBMA) is a technique increasingly used in drug development for synthesising results from multiple studies, allowing pooling of information on non-linear dose-response and time-course characteristics. Such analyses can substantially increase the power to detect small but potentially clinically significant effects and can be used to support decision-making and inform future trial designs. We have extended this technique to multiple treatment-comparisons by incorporating methods for network meta-analysis into MBMA models, using a Bayesian approach. Our model-based network meta-analysis (MBNMA) preserves randomisation by aggregating within-study relative effects, and allows for formal testing of consistency between direct and indirect evidence for dose-response and time-course models. We illustrate this approach using an example from time-course MBNMA and highlight the value of these techniques in both drug development and for reimbursement agencies.

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05 June 2018

Mario Ouwens - Overall Survival and Progression Free Survival extrapolation is important for HTA submissions for cancer treatments to UK NICE and other cost utility analysis country HTAs. Clinical opinion, historical trials and Real World Evidence are used to evaluate the fit of the distributions and to select the distributions most in line herewith. In previous work presented at ISPOR Europe 2017, we showed that different distributions can fit the Kaplan Meier equivalently well, resulting in about the same estimated average survival for the trial period, while the extrapolated part was different depending on which distribution we chose. As such, it is important to consider the extrapolation part in detail when needing it for HTA submissions.

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Overall Survival and Progression Free Survival extrapolation is important for HTA submissions for cancer treatments to UK NICE and other cost utility analysis country HTAs. Clinical opinion, historical trials and Real World Evidence are used to evaluate the fit of the distributions and to select the distributions most in line herewith. In previous work presented at ISPOR Europe 2017, we showed that different distributions can fit the Kaplan Meier equivalently well, resulting in about the same estimated average survival for the trial period, while the extrapolated part was different depending on which distribution we chose. As such, it is important to consider the extrapolation part in detail when needing it for HTA submissions.

Objective:

Methodology is developed to incorporate clinical opinion in the estimation process using prior distributions, rather than only using it to assess face validity.

Results:

Using prior distributions for the survival probability at certain time points, the parameters of standard distributions like exponential, Weibull, Gompertz, Generalized Gamma, lognormal, loglogistic and extensions with spline and cure models were estimated for Keynote 24. Different assumptions were formulated into prior distributions related to the hazard ratio at 5 years and 10 years, given that the ratio between hazards stayed the same, that it declined, and that there was no treatment effect remaining.

Conclusion:

By using prior distributions, it is possible to take clinical opinion around long-term survival into account in the estimation process leading to more reasonable extrapolations.

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05 June 2018

Shijie (Kate) Ren - Meta-analyses using fixed effect and random effects models are commonly applied to synthesise evidence from randomised controlled trials in health technology assessment. The models differ in their assumptions and the interpretation of the results. Fixed effect models are often used because there are too few studies with which to estimate the between-study standard deviation from the data alone, but not that heterogeneity is unlikely to be expected. The aim is to propose a framework for eliciting an informative prior distribution for the between-study standard deviation in a Bayesian random effects meta-analysis model to genuinely represent heterogeneity when data are sparse.

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Background:

Meta-analyses using fixed effect and random effects models are commonly applied to synthesise evidence from randomised controlled trials in health technology assessment. The models differ in their assumptions and the interpretation of the results. Fixed effect models are often used because there are too few studies with which to estimate the between-study standard deviation from the data alone, but not that heterogeneity is unlikely to be expected.

Objectives:

The aim is to propose a framework for eliciting an informative prior distribution for the between-study standard deviation in a Bayesian random effects meta-analysis model to genuinely represent heterogeneity when data are sparse.

Methods:

We developed an elicitation method using external information such as empirical evidence and experts’ beliefs on the ‘range’ of treatment effects in order to infer the prior distribution for the between-study standard deviation. We also developed the method to be implemented in R.

Results:

The three-stage elicitation approach allows uncertainty to be represented by a genuine prior distribution to avoid making misleading inferences. It is flexible to what judgments an expert can provide, and is applicable to all common types of outcome measure.

Conclusions:

The choice between using a fixed effect or random effects meta-analysis model depends on the inferences required and not on the number of available studies. Our elicitation framework captures external evidence about heterogeneity and overcomes the often implausible assumption that studies are estimating the same treatment effect, thereby improving the quality of inferences in decision making.

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05 June 2018

Jonathon Alsop - We compared methods for an indirect treatment comparison (ITC) of two marketed medicines in patients with diabetic macular edema. Post-stratification, inverse probability weighting based on simulated data, weight optimization, and regression model techniques were used to compare pooled individual patient-level data (IPD) from two phase III studies with summary-level data from two other phase III studies. The impact of adjusting for up to two important baseline characteristics was assessed. The weight optimisation method used is a new matching-adjusted indirect treatment comparison (MAIC) method we developed, which allows for more flexible and sophisticated matching compared with current MAIC approaches. We describe how this method works, how it can be applied (in both R and SAS), and how it compared with the other, more standard, methods.

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04 June 2018

Mark Belger - Following the launch of a new intervention, information is often required to understand how the new intervention is performing outside of the clinical trial setting. Comparative effectiveness studies using retrospective databases will answer some of these questions. However, these are often limited in the information they have available, and so prospective observational studies may be considered.

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04 June 2018

Stijn Vansteelandt - The LEADER trial found protective cardiovascular effects of liraglutide compared to placebo in patients with Type II diabetes and high cardiovascular risk. Effects were also found on glycated haemoglobin, body weight, blood pressure and heart rate, thereby raising the question to what extent these potential pathways my explain liraglutide's protective effect. We will explain how we addressed this question by expanding modern techniques from causal mediation analysis. In particular, we will show how to identify and infer the natural indirect of liraglutide on the time to major cardiovascular events via the repeatedly measured glycated haemoglobin levels. The considered proposal addresses complications due to patients dying before the mediator is assessed, due to the mediator being repeatedly measured, and due to post-treatment confounding of the effect of glycated haemoglobin by other mediators, which makes mediation analysis a challenging enterprise.

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04 June 2018

Carl-Fredrik Burman - Regulatory guidance says that α=5% is “conventional” but that alternative values “may be acceptable or even preferable in some cases”. Historically, the value 5% can be traced back to Fisher, who was clear that this is an arbitrary choice. In this presentation, we will try to optimize the Type I Error, based on a total public health perspective. We conclude that the Type I Error should be different for different clinical trials, reflecting the size of the trial and the rarity of the disease.

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04 June 2018

Nigel Stallard - Conventional power calculations for a confirmatory clinical trial may lead to a sample size that is infeasible or even impossible to achieve if the trial is in a rare disease or other small population. This suggests that choice of an appropriate sample size should, in some way, reflect the size of the population for which the investigational intervention is intended. One method by which this might be achieved is the value-of-information approach whereby the cost of data collection can be weighed against the gains from the improved decision-making based on the information obtained. In this talk explores the use of such a method in the rare disease setting, showing how this approach can lead to justification of smaller trials when the patient population is smaller.

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04 June 2018

Kristina Weber - In planning a clinical trial for demonstrating the efficacy of pioglitazone to resolve leucoplakia and erythroplakia in Fanconi anemia patients the need for a randomized controlled trial particularly under sample-size restrictions had to be discussed as very promising results were available from a single-arm clinical trial. The assumption of knowing the counterfactual is easily violated in single-arm trials and it is not possible to test it. Furthermore, undocumented and undetected patient selection is one of the main downfalls of single-arm trials. Contrary to the common opinion, addressing a new research question with single-arm trials requires more patients in the long run than starting with a (small) RCT from the beginning. Therefore, we contrast a single-arm based research strategy with a decision making strategy based on RCTs, which can be viewed (and used) as Lego®-type building blocks.

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04 June 2018

Quick fire single slide presentations of the posters included in the poster session.

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04 June 2018

Maria Costa - Consider the following scenario: a subject with a specific illness presents to a physician who has a choice between two medicines she could prescribe. The physician will need to predict which intervention has a more favourable benefit-risk for the subject at hand. To achieve an informed decision, she measures a series of additional data, or covariates, which have been shown to impact the benefit-risk profile of the medicines. In this talk we investigate the performance of a decision-rule for personalised benefit-risk assessment. Given a set of covariates, and a joint model for the efficacy and safety responses, results from a simulation study show that the decision-rule has good performance overall, giving high probability of proposing a specific treatment only to the subgroup of patients for which the treatment is known to have a positive benefit-risk profile.

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04 June 2018

Shahrul Mt-Isa - Focusing on clinical aspects, a benefit-risk assessment (BRA) of a healthcare product can directly inform health technology assessment (HTA). HTA is multidisciplinary; and examines broader aspects of health technology including safety, effectiveness, cost-effectiveness, and various social aspects. BRA is a fast developing area. Among recent development, patient focused BRA has been playing an increasingly important role in regulatory and HTA perspective.

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Focusing on clinical aspects, a benefit-risk assessment (BRA) of a healthcare product can directly inform health technology assessment (HTA). HTA is multidisciplinary; and examines broader aspects of health technology including safety, effectiveness, cost-effectiveness, and various social aspects. BRA is a fast developing area. Among recent development, patient focused BRA has been playing an increasingly important role in regulatory and HTA perspective.

HTA agencies have already been applying qualitative and quantitative BRA approaches during their evaluation of new drug submissions for pricing and reimbursement approvals. Structured and quantitative approaches, visual tools, decision support tools, and multi-criteria decision analysis (MCDA) methods have been well developed. However, many of these methods are for policy makers, and may not be appropriate for patient focused BRA.

The most commonly used method for patient focused BRA is the discrete choice experiment (DCE), which presents a number of statistical challenges in terms of experimental design, data analysis and properly using the results for decision making.
In this session, we will present an up-to-date review of recent development including those summarized in report from the EFSPI joint working group of BRA and HTA SIGs, and then discuss in more detail HTA and BRA approaches. Our focus will be the statistical and empirical aspects of approaches for patient focused BRA, in particular, DCE, and their application in HTA. An example will be used to illustrate approaches to the design and analysis of a DCE in a typical scenario in which patient focused BRA may play a key role in BRA and HTA. 

The views expressed herein represent those of the presenters and do not necessarily represent the views or practices of their companies or the views of the general Pharmaceutical Industry. The work presented here is a voluntary effort of the members of the EFSPI Joint BR-HTA SIG.

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04 June 2018

Sophie Dimonaco - Actemra is approved around the world for patients with RA and has a well characterized safety profile. A phase III trial evaluating Actemra in patients with Giant Cell Arteritis (GCA) showed strong efficacy in Actemra plus steroids over Placebo plus steroids, but with a slightly differing safety profile to that of RA. In order to evaluate the differences in safety profiles between RA and GCA, the clinical data team and our Real-World Data Science (RWD-S) group collaborated together to compare data from different sources.

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Actemra is approved around the world for patients with RA and has a well characterized safety profile. A phase III trial evaluating Actemra in patients with Giant Cell Arteritis (GCA) showed strong efficacy in Actemra plus steroids over Placebo plus steroids, but with a slightly differing safety profile to that of RA. In order to evaluate the differences in safety profiles between RA and GCA, the clinical data team and our Real-World Data Science (RWD-S) group collaborated together to compare data from different sources.

The RWD-S team performed a detailed analysis of incidence rates of safety events in patients with GCA and patients with RA that had never received Actemra, from a US-based general healthcare claims database. We then compared the real-world incidence rates to those seen in the Actemra clinical development program to contextualize the observed safety profile of Actemra in GCA and determine whether the differences seen were acceptable based on the population studied. I will discuss the methods and results of this collaborative study as well as some lessons learnt.

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04 June 2018

David Oakley - Hospital resources such as beds, theatre time, and staff are frequently shared between emergency patients, and elective patients whose care requirements are known in advance. This poses a logistical challenge; some portion of each resource must be set aside for emergency patients when planning the number and type of elective admissions. Discrete event simulation (DES) models in healthcare can emulate the randomness seen in real systems, at a level of detail which is necessary for models to be convincing. An Online Discrete Event Simulation (ODES) takes all the components of a DES model, and adds the ability to load the state of the real system to make predictions about how it might evolve in the short-term.

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Discrete event simulation (DES) models in healthcare can emulate the randomness seen in real systems, at a level of detail which is necessary for models to be convincing. An Online Discrete Event Simulation (ODES) takes all the components of a DES model, and adds the ability to load the state of the real system to make predictions about how it might evolve in the short-term. We describe the development of a whole-hospital, proof-of- concept ODES to assess the impact of elective admissions decisions on wards which are shared with emergency patients. The model is parameterised using 18 months of patient administrative data.

Since ODES is a relatively new method, this research focuses on formalising the model development process, including the validation of conditionally distributed simulation outputs. Additionally, a new probabilistic routing method is developed to better represent inter-ward dependencies during peaks in bed demand, by analysing the relationship between ward transfers and ward occupancy. The results are used to parameterise “Dynamic Transition Matrices”. Finally, we demonstrate how additional patient-level, made available after admission, can affect the predicted bed census. The case is made for further use of this type of information, as part of an operational ODES model.

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04 June 2018

Guiyuan Lei - Predicting the survival for cancer patients is important for informative medical decision making. When machine learning is applied more and more into different areas, it is appealing to evaluate at what extent we can predict survival for cancer patients from electronic health record data. This presentation will use one real example to discuss the advantage of the real world data and challenge of the problem including the quality of the electronic health data, sample size and imbalance of data. We will also show the performance of different models/approaches, from simple model to advanced machine learning.

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04 June 2018

Jessica Davies - This research aims to examine the use of electronic health record (EHR) data to derive effectiveness evidence.

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OBJECTIVES:

This research aims to examine the use of electronic health record (EHR) data to derive effectiveness evidence.

METHODS:

EHR data collected from January 1, 2011 and February 28, 2016 (control cohort) was combined with individual patient data (IPD) from two single-arm phase II studies (intervention cohort) to estimate treatment effectiveness. The data was harmonized by applying the inclusion and exclusion criteria from the trial to the EHR database. Prognostic variables were selected a priori and three different methods (propensity score matching (PSM), inverse probability of treatment weighting (IPTW), and Genetic Matching (GenMatch)) were applied and compared. A multivariate Cox proportional hazards model was used to compare OS in methods that achieved covariate balance. A sensitivity analysis evaluating the survival of the control cohort from EHR was conducted with an indirect comparison between the control cohort and digitized trial data from the control treatment.

RESULTS:

After applying the inclusion and exclusion criteria, the intervention cohort (n=183) and control cohort (n=72) were imbalanced in terms of measured confounders. The PSM method did not balance measure confounders (standardize mean differences (SMDs ≥25%) and the IPTW and GenMatch method improved imbalance (SMDs <10%). The observed treatment effect on the risk of death of the IPTW (HR=0.64, 95% CI 0.48-0.88) and GenMatch adjusted analyses (HR=0.54, 95% CI=0.48-0.62) was similar. Median OS was similar between the EHR control cohort (15.6 months) and clinical control cohort (14.9 months).

CONCLUSIONS:

Our results demonstrate the utility of EHR data to estimate comparative effectiveness in a single-arm trial setting.

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04 June 2018

Thomas Zwingers - Asthma is one of the most common chronic conditions in the world and it is a major public health problem. Recent studies suggest that persistent uncontrolled inflammation in the peripheral small airways can also contribute to clinical expression and worse control of asthma. The assessment and monitoring of small airways dysfunction (SAD) in asthma is a challenging matter because the area is relatively inaccessible for functional measurements. Several tests are available to assess different aspects of SAD and the results and limitations of each test have been recently extensively reviewed, but unfortunately none of them was able to measure the presence of SAD. Given these assumptions, a Structural Equation Model (SEM) was built to go beyond these limits and to provide a general tool in which the combination of clinical methods are used for SAD detection.

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Asthma is one of the most common chronic conditions in the world and it is a major public health problem. Recent studies suggest that persistent uncontrolled inflammation in the peripheral small airways can also contribute to clinical expression and worse control of asthma.

The assessment and monitoring of small airways dysfunction (SAD) in asthma is a challenging matter because the area is relatively inaccessible for functional measurements. Several tests are available to assess different aspects of SAD and the results and limitations of each test have been recently extensively reviewed, but unfortunately none of them was able to measure the presence of SAD.

Given these assumptions, a Structural Equation Model (SEM) was built to go beyond these limits and to provide a general tool in which the combination of clinical methods are used for SAD detection. SEM is a form of graphical and causal modeling in which relationships and complex-causal hypotheses about the phenomenon of interest can be represented in either graphical or equational form.

In the non-interventional study we explored, SAD was considered as a latent variable (not directly measurable) and the relationships among SAD and other clinical methods (observed variables) have been hypothesized and tested following an exploratory/confirmatory model-building approach.

The final cross-sectional model was tested at each post baseline visit in order to build an overall longitudinal SEM model, where the SAD change during the study was explored. The group classification (SAD vs Non-SAD) was performed through a latent transition analysis (LTA) based on the SEM scores.

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04 June 2018

Alessandro Previtali - Delayed treatment effect (DTE), cure fraction (CF) and treatment switching (TS) are known to cause the violation of the proportional hazards assumption required in standard analyses of time-to-event data. We investigated their effect on power under different simulation conditions for various data distributions, durations of the study, accrual periods and analysis methods. We investigated their impact on sample size and finally proposed Bayesian methods that integrate DTE, CF and/or TS at design stage.

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Delayed treatment effect (DTE), cure fraction (CF) and treatment switching (TS) are known to cause the violation of the proportional hazards assumption required in standard analyses of time-to-event data. We investigated their effect on power under different simulation conditions for various data distributions, durations of the study, accrual periods and analysis methods. We investigated their impact on sample size and finally proposed Bayesian methods that integrate DTE, CF and/or TS at design stage.
Simulations show that power decreases dramatically in the presence of DTE and TS, whereas overpowered situations occur when CF is only encountered in the experimental group. Underpowered situations occur when CF is experienced in both the experimental and control groups. Mixed scenarios are observed when DTE, CF and/or TS are analysed in combination. When DTE, CF and/or TS are considered at design stage and integrated into the sample size calculation, an adjustment in the number of subjects may be required to maintain the targeted power.

Sample size re-estimation and/or dependent censoring analysis methods can be also used. Alternatively, Bayesian methodologies can be proactively implemented at design stage to account for potential DTE, CF and/or TS. In conclusion, omitting DTE, CF and/or TS at the design stage could lead to drastic implications on power and significantly impact the chance to appropriately identify the existence of a potential treatment effect. The implementation of Bayesian methodologies during the design stage is recommended in order to proactively anticipate the effect of DTE, CF and/or TS, allowing for a more efficient design.


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04 June 2018

James Bell - The a priori trial planning assumption of non-proportional hazards is becoming more common with the rise of immuno-oncology. However, standard analytic methods of sample size calculation are not compatible with time-to-event trials designed under this assumption. Although the log-rank test and Cox analysis are still regularly required for these trials, the Schoenfeld and Freedman formulae for calculating required event numbers both rely upon a constant Hazard Ratio. For Restricted Mean Survival Time and Landmark Analysis, which are better suited to analysing non-proportional hazards, current analytic sample size methods also struggle to accurately account for censoring. This talk will present novel,accurate, analytic methods of hazard ratio prediction and power calculation for all three testing strategies with minimal distributional requirements for events, censoring and recruitment. A single, fast and flexible implementation in R will also be presented, including validation of results by simulation.

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04 June 2018

Dominic Magirr - The Kaplan-Meier plots from some (but not all) recent trials of immunotherapy versus chemotherapy have shown a delayed separation of survival curves. When considering design options for a new study of this type, there is a concern that the standard proportional hazards assumption will lead to an inappropriate design with a high risk of type II error. The first part of my talk will consider sensible choices of stopping boundary in this situation. An alternative approach would be to assess the evidence for delayed separation, directly, at an interim analysis, and use this information to adapt the design. I will examine whether such an adaptive strategy leads to real gains compared to a fixed group-sequential trial.

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09 May 2018

Lisa Hampson & Carl-Fredrik Burman: Regulatory agencies require strong control of the Type I Error for all confirmatory statistical tests. This introductory lecture looks at multiplicity issues and how they can arise for several reasons, e.g.: the possibility of winning on two different endpoints (like PFS and OS), interim analyses, or testing for effects in several patient groups. The impact of multiplicity is covered and error rates and power are defined.

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09 May 2018

Lisa Hampson & Carl-Fredrik Burman: Regulatory agencies require strong control of the Type I Error for all confirmatory statistical tests. The second lecture in this collection reviews how to maintain strong control of the family-wise error rate, the concept of recycling (including road maps), the Holm procedure, closed testing, logic restrictions and the pooled test.

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09 May 2018

Lisa Hampson & Carl-Fredrik Burman: Regulatory agencies require strong control of the Type I Error for all confirmatory statistical tests. This third lecture covers definitions of power (1-dimensional power, multi-dimensional power, disjunctive power and conjunctive power) with examples using different Bonferroni weights. Power comparisons for Bonferroni, Holm, Dunnett and Hochberg tests are discussed.

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09 May 2018

Lisa Hampson & Carl-Fredrik Burman: Regulatory agencies require strong control of the Type I Error for all confirmatory statistical tests. This fourth lecture covers GSDs monitoring a single endpoint, including one-sided and two-sided group sequential testing, error spending tests and calculation of repeated confidence intervals. GSDs monitoring multiple endpoints or treatments are also discussed.


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09 May 2018

Lisa Hampson & Carl-Fredrik Burman: Regulatory agencies require strong control of the Type I Error for all confirmatory statistical tests. This fifth lecture covers using decision analysis to optimise multiple testing, for example using utility functions to find the optimal weights for multiple endpoints (e.g. PFS and OS) and using time-functions for utility to decide how many interim analyses to have.

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08 May 2018

Clinical Trials are increasingly featuring a wide variety of Patient Reported Outcomes (PROs). PROs are important endpoints to both regulatory and health technology bodies in their assessments and approvals. There are standard ways in which PRO instruments are developed and validated, as well as important concepts for designing and interpretation of PRO data in clinical trials such as assessing the Minimally Clinically Important Differences (MCIDs/MIDs) and the use of a Responder definition. The intent of this webinar is to describe an overview of how PROs tools are developed and used in clinical trial settings and how results can be clinically and meaningfully interpreted. In addition, a regulatory viewpoint will be shared on the key considerations for PROs. The talks will also include examples and case-studies showing the derivation of a validated thresholds for treatment responder and interpretation of PROs in the context of regulatory approval.

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Kim Cocks (Adelphi Values)

Kim Cocks is a Director and Principal Statistician at Adelphi Values, a global healthcare consultancy. She has worked as a medical statistician and clinical trial methodologist for over 20 years across pharmaceutical, academic and consultancy environments. She specialises in PRO interpretation and analysis and is an active member of both the EORTC Quality of Life group and ISOQOL.

Abstract
Overview of PROs and clinically meaningful interpretation

PROs are validated instruments designed to provide a direct report from the patient on aspects of their health including symptoms and functioning. This talk will provide a brief overview of how PROs are developed, how they provide a score for multi-dimensional aspects of a patient’s health and why these can be difficult to interpret. The variety of quantitative and qualitative methods available to aid interpretation will be introduced.  

Andrew Thomson (EMA)

Andrew Thomson is a statistician at the EMA, in the Human Medicines Research & Development Support  Division. Prior to joining the EMA in 2014, he spent 7 years at the MHRA, initially as a Statistical Assessor in the Licensing Division, and subsequently Head of Epidemiology in the Vigilance & Risk Management of Medicines Division.

Abstract
Regulatory Considerations for PROs

In this talk I will present some regulatory considerations on the use of PROs in regulatory assessment, and how they can be developed. These will be discussed alongside a recent case example that has been through the qualification procedure at EMA.

Christoph Gerlinger (Bayer)

Christoph Gerlinger is Bayer’s Expert Statistician for Health Technology Assessment and Women’s Health. He is the regulatory chair of the European Federation of Statisticians in the Pharmaceutical Industry (EFSPI) and a work package leader for the IMI BigData@Heart project. Christoph worked as statistician over 25 years in the pharmaceutical industry and in his spare time he teaches at the University Medical School of Saarland.

Abstract
Empirical derivation of the minimal important difference for PROs

In this talk I will present some methods on how to derive the minimal important difference for PROs from a clinical trial. I will present a worked example where we used this empirical minimal important difference to formulate a responder definition that was then accepted by the FDA.

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26 April 2018

This journal club features two papers on the topic of Modelling and Simulation. Please join us to hear Michael O’Kelly (IQVIA) and Carl-Fredrik Burman (Astrazeneca) present their recent work.

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The Chair will be Vladimir Anisimov (Amgen).

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18 April 2018

Trials designed to answer a range of questions, often incorporating multiple treatment arms are increasingly being considered in all phases of clinical research in both the pharmaceutical industry and public sector trials. A variety of terms including platform, umbrella and basket designs have been used to describe particular versions of this general framework. During this webinar a regulatory speaker will consider what is meant by the different terms and potential regulatory hurdles, while speakers from industry and the public sector will share their practical experiences of these types of trials.

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Presenters:

Mahesh Parmar (Medical Research Council), James Matcham (AstraZeneca) and Julia Saperia (Medicines and Healthcare products Regulatory Agency)

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17 April 2018

Dr Bernd-Wolfgang Igl, Principal Statistician at Bayer, will be presenting on Statistical Analysis of the Comet-Assay.

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The single cell gel electrophoresis assay, also known as Comet assay, is a widespread test within genetic toxicology and sensitive to detect chemically induced DNA damages in various tissues. This webinar will start with an introduction to the toxicological background and the test principle itself, including a description of the standard experimental design. Different statistical strategies will then be presented; the focus will be on the in-vivo Comet assay. Finally, the use of median instead of average tail intensities per slide will be described and discussed; this is suggested in the updated OECD Guideline for the Testing of Chemicals No. 489, thus directly affecting the statistical analysis.   

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22 March 2018

Presentations: 1. 'Looking over the Fence' by Richard Pugh 2. 'The Use of Predictive Modelling in Customer Relationship Management' by Joachim Schwarz 3. 'Moving from R&D to Sales and Marketing: the business analytics experience of a statistician at Lilly' by Todd Sanger

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About the Presenter: Dr Joachim Schwarz

Dr Joachim Schwarz, studied mathematics at the Georg August University in Göttingen and at Trinity College in Dublin. He did his PhD in business administration at the private university of Witten / Herdecke, and afterwards, he has more than nine years working experience as manager and team leader in the analytical CRM department of the Deutsche Telekom, with special focus on data mining and predictive modelling. Since winter term 2013, he is professor for business mathematics and statistics at the FOM university of applied sciences in Bonn.

Abstract

The webinar focusses on one main problem of every customer relationship management department: How to identify those customers, which are more likely to e.g. terminate their customer relationship or to buy a new product? One way to solve this is predictive modelling. We will have a look on typical data a company has about their customers, and how it can be used to develop a model to predict a specific customer behaviour.  A special focus will be laid on limitations of this approach and, last but not least, its specific potential to generate or to save money.

 

About the Presenter: PhD Todd Sanger

Research Fellow, Advanced Analytics at Eli Lilly and Company.

Abstract

Typically, pharmaceutical companies invest more money on sales and marketing than they do on R&D, yet very few statisticians work to support sales and marketing organizations.  At Lilly, we created a group of statisticians to support analytical problems in Sales and Marketing.  This talk will describe the types of issues we encounter and the statistical techniques we use to tackle these issues.

 

About the Presenter: Richard Pugh

Richard Pugh is Chief Data Scientist and co-Founder of Mango Solutions, a Data Science consulting company specialised in the pharmaceutical industry.  Richard studied Mathematics and Statistics at the University of Bath before working as a biostatistician within the life sciences industry.  Richard then joined Insightful, working as a Consultant across many industries around the application of statistical methods using the S-PLUS software product.  In 2002, Richard co-founded Mango Solutions to focus on the application of analytics to solve business challenges using technologies such as SAS, S-PLUS and R.  Richard is heavily involved in the R community, co-authoring the book “R in 24 Hours”, and was the first President of the R Consortium.  Richard is an active member of the committee of the RSS Data Science Section.  Today, Richard spends much of his time advising clients across a variety of industries on data-driven approaches, and is a regular speaker at analytic conferences.

Abstract

The last 10 years have seen significant growth in companies investing in Big Data, Data Science, Machine Learning and AI.  The key driver for organisations investing in these initiatives is to generate insight from data that can be used to drive better decision making.  However, as each industry has different aims and constraints, the adoption of data-driven approaches can vary significantly. 

This presentation will look at core concepts of data science, such as the 3 Vs of data, and how different industries have looked to implement these concepts.  In particular, we will look at possible opportunities for the pharmaceutical sector to adapt successful approaches from other industries.

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13 March 2018

This webinar proposes a very concrete illustration of MCDA and of the extended models SMAA and Dirichlet SMAA using case-studies. We will present how to derive a benefit-risk utility score for each treatment, how to compare several treatments, how to present the results and how to conduct sensitivity analyses. The differences between the models will be highlighted, and some R code will be presented and shared after the presentation.

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Abstract

Several quantitative methodologies have been proposed to support decision-making in drug development. In particular, MultiCriteria Decision Analysis (MCDA) is a useful tool to assess the benefit-risk balance of medicines according to the performances of the treatments on several criteria, accounting for the preferences of the decision-makers regarding the relative importance of these criteria. The EMA Benefit-Risk Methodology Project suggested that it is one of the most comprehensive among the quantitative methodologies they considered, and it is also recommended by several highly profiled expert groups. While MCDA requires the exact elicitation of the weights of the criteria according to the preferences of the decision-makers, extended versions of MCDA have been proposed, such as Stochastic Multicriteria Acceptability Analysis (SMAA) and Dirichlet SMAA, where the weights are considered as random variables to account for some uncertainty in the weight assignment.

About the Presenter: Gaelle Saint-Hilary

Gaelle Saint-Hilary works in statistics for the pharmaceutical industry since 2006. She is currently completing a PhD on “Quantitative Decision-Making in Drug Development”, sponsored by Servier, at the Polytechnic University of Turin (Italy). Before that, she worked as biostatistician in the industry, first at Servier for 5 years and then at Novartis Oncology for 4 years. She was responsible for the clinical development and the licensing of medicinal products in neuropsychiatry and leukemia, and her main scientific interests were benefit-risk assessment, network meta-analyses, multiple test procedures, simulation models of time-to-event data and survival analysis in presence of intercurrent events. The development and the promotion of quantitative methods for drug benefit-risk assessments is one of the major topics she considers during her PhD, with the final goal of enhancing decision-making throughout the drug life-cycle. 

About the Presenter: Stephanie Cadour

Graduated in 2011, Stéphanie Cadour works as a biostatistician at Servier (France) since then. She was initially responsible for the statistical aspects of phase II and III clinical studies conducted in the therapeutic areas of neuropsychiatry and diabetes. She is now working on early phase studies in the field of oncology. In parallel of these activities, Stephanie developed skills on meta-analyses as well as on quantitative approaches for benefit-risk assessment on which she has been working on since 2011.

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06 March 2018

Early benefit assessment was introduced in Germany in 2011 as a basis for price negotiations between payers and pharmaceutical companies. Since then, all new drug substances have to be assessed at the Federal Joint Committee (G-BA), by indication. This series of webinars by Dr C. Schwenke will focus on the statistical implications and how to deal with the requirements by G-BA and their methodological support institute IQWiG and should be of particular interest to statisticians who work in HTA and those who deal with requests from their local German team.

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Abstract

The so called early benefit assessment in Germany was introduced in 2011 as basis for price negotiations of the institutionary sick funds and the pharmaceutical company. Since then, all new drug substances are to be assessed at the Federal Joint Committee (G-BA, Gemeinsamer Bundesausschuss) by indication. A new indication always requires a new procedure. In a first step, the additional benefit over a comparator has to be shown based on the rules of evidence based medizine and the available clinical data. The marketing authorization holder has to submit a benefit dossier with all available clinical data for the drug substance in the indication. A template for the dossier is provided by G-BA and defines how the data is to be shown. This template has statistical implications with regards to the presentation of the clinical data including subgroup analyses, surrogate endpoints, direct and indirect comparisons, metaanalyses and others.

The web-seminar will focus on the statistical implications and how to deal with the requirements by G-BA and their methodological support institute IQWiG. PROs and CONs of certain statistical methods will be discussed in the light of their acceptance by G-BA and IQWiG. The target audience will be statisticians in HTA and statisticians who cope with the requests from their local German affiliate. 

About the Presenter: Dr. Carsten Schwenke

Dr. Carsten Schwenke studied statistics at the Universities of  Dortmund and Sheffield (UK) with minor subject theoretical  medicine (University of Bochum). He completed his studies with a diploma in statistics and gained the certificate Biometry of the  University of Dortmund. He received his PhD from the Technical  University Berlin in the area public health / health economics at  the Berlin School of Public Health.

Dr. Schwenke works as a statistician since 1995, first as a  statistical researcher at the statistical consultation center of the University of Dortmund and in the department medical statistics at the University of Göttingen. This was followed by about 10 years as a project biometrician at Chiron-Behring in Marburg, where he headed the biometry, and at Schering AG. After this, he worked as project leader Specialized Therapeutics in the department of Global Health Economics and Outcomes Research at Bayer-Schering Pharma AG in Berlin.

Dr. Schwenke founded SCO:SSiS in 2007. Main areas of work are clinical development and – particularly since introduction of the AMNOG in 2011 – the area of market access and benefit assessment. A list of publications can be found in Medline (http://www.ncbi.nlm.nih.gov/pubmed/?term=Schwenke+C). 

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20 February 2018

Broad toxicology profiling traditionally takes place at the interface between discovery and development when a potential drug candidate is selected. However, it would be both time and cost-wise better if mechanism (target)-related toxicity and compound-chemistry related toxicity is addressed earlier, when discussions on novel drug targets take place and compound series are identified and optimized. As the traditional in-vivo and in-vitro toxicity testing is rather low-throughput, they can't be used in these early stages of the drug discovery process. Therefore a paradigm shift in toxicity testing needs to take place to move to high-throughput cell-based assays to reveal key pathways and proteins linked with toxicity end points. I will present some explorations and case studies where both transcriptional profiling and imaging techniques are explored to flag early potential toxicity issues already during the drug development process where the findings could still influence the final candidate selection.

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About the Presenter: Bie Verbist

Bie Verbiststudied medicinal chemistry at KU Leuven, Belgium and finished PhD in 2005 on the design and synthesis of potential β‐turn mimetics in the group of Prof.Dr.G.Hoornaert. Following this, she started as a post-doc at Johnson & Johnson Pharmaceutical Research and Development in Beerse, Belgium where she was involved in the design, synthesis and validation of new biological entities within the therapeutic areas pain and internal medicine, for three years. Afterwards, she went back to university to follow a one-year MaNaMa in statistical data analysis. In 2011, after a short period of working as a scientific collaborator at Ghent University on qPCR data, she started a second PhD to search for low-frequency variants in viral populations using Illumina deep sequencing technologies under supervision of Prof.Dr. O. Thas and in close collaboration with Johnson & Johnson Pharmaceutical Research and Development in Beerse, Belgium. In 2014, Bie joined Johnson & Johnson as a Principal Biostatistician in the non-clinical statistics department to support oncology projects within discovery with a focus on omics data analysis. 

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31 January 2018

Early benefit assessment was introduced in Germany in 2011 as a basis for price negotiations between payers and pharmaceutical companies. Since then, all new drug substances have to be assessed at the Federal Joint Committee (G-BA), by indication. This series of webinars by Dr C. Schwenke will focus on the statistical implications and how to deal with the requirements by G-BA and their methodological support institute IQWiG and should be of particular interest to statisticians who work in HTA and those who deal with requests from their local German team.

Read more...

Abstract

The so called early benefit assessment in Germany was introduced in 2011 as basis for price negotiations of the institutionary sick funds and the pharmaceutical company. Since then, all new drug substances are to be assessed at the Federal Joint Committee (G-BA, Gemeinsamer Bundesausschuss) by indication. A new indication always requires a new procedure. In a first step, the additional benefit over a comparator has to be shown based on the rules of evidence based medizine and the available clinical data. The marketing authorization holder has to submit a benefit dossier with all available clinical data for the drug substance in the indication. A template for the dossier is provided by G-BA and defines how the data is to be shown. This template has statistical implications with regards to the presentation of the clinical data including subgroup analyses, surrogate endpoints, direct and indirect comparisons, metaanalyses and others.

The web-seminar will focus on the statistical implications and how to deal with the requirements by G-BA and their methodological support institute IQWiG. PROs and CONs of certain statistical methods will be discussed in the light of their acceptance by G-BA and IQWiG. The target audience will be statisticians in HTA and statisticians who cope with the requests from their local German affiliate. 

About the Presenter: Dr. Carsten Schwenke

Dr. Carsten Schwenke studied statistics at the Universities of  Dortmund and Sheffield (UK) with minor subject theoretical  medicine (University of Bochum). He completed his studies with a diploma in statistics and gained the certificate Biometry of the  University of Dortmund. He received his PhD from the Technical  University Berlin in the area public health / health economics at  the Berlin School of Public Health.

Dr. Schwenke works as a statistician since 1995, first as a  statistical researcher at the statistical consultation center of the University of Dortmund and in the department medical statistics at the University of Göttingen. This was followed by about 10 years as a project biometrician at Chiron-Behring in Marburg, where he headed the biometry, and at Schering AG. After this, he worked as project leader Specialized Therapeutics in the department of Global Health Economics and Outcomes Research at Bayer-Schering Pharma AG in Berlin.

Dr. Schwenke founded SCO:SSiS in 2007. Main areas of work are clinical development and – particularly since introduction of the AMNOG in 2011 – the area of market access and benefit assessment. A list of publications can be found in Medline (http://www.ncbi.nlm.nih.gov/pubmed/?term=Schwenke+C). 

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23 January 2018

Early benefit assessment was introduced in Germany in 2011 as a basis for price negotiations between payers and pharmaceutical companies. Since then, all new drug substances have to be assessed at the Federal Joint Committee (G-BA), by indication. This series of webinars by Dr C. Schwenke will focus on the statistical implications and how to deal with the requirements by G-BA and their methodological support institute IQWiG and should be of particular interest to statisticians who work in HTA and those who deal with requests from their local German team.

Read more...

Abstract

The so called early benefit assessment in Germany was introduced in 2011 as basis for price negotiations of the institutionary sick funds and the pharmaceutical company. Since then, all new drug substances are to be assessed at the Federal Joint Committee (G-BA, Gemeinsamer Bundesausschuss) by indication. A new indication always requires a new procedure. In a first step, the additional benefit over a comparator has to be shown based on the rules of evidence based medizine and the available clinical data. The marketing authorization holder has to submit a benefit dossier with all available clinical data for the drug substance in the indication. A template for the dossier is provided by G-BA and defines how the data is to be shown. This template has statistical implications with regards to the presentation of the clinical data including subgroup analyses, surrogate endpoints, direct and indirect comparisons, metaanalyses and others.

The web-seminar will focus on the statistical implications and how to deal with the requirements by G-BA and their methodological support institute IQWiG. PROs and CONs of certain statistical methods will be discussed in the light of their acceptance by G-BA and IQWiG. The target audience will be statisticians in HTA and statisticians who cope with the requests from their local German affiliate. 

About the Presenter: Dr. Carsten Schwenke

Dr. Carsten Schwenke studied statistics at the Universities of  Dortmund and Sheffield (UK) with minor subject theoretical  medicine (University of Bochum). He completed his studies with a diploma in statistics and gained the certificate Biometry of the  University of Dortmund. He received his PhD from the Technical  University Berlin in the area public health / health economics at  the Berlin School of Public Health.

Dr. Schwenke works at a statistician since 1995, first as a  statistical researcher at the statistical consultation center of the University of Dortmund and in the department medical statistics at the University of Göttingen. This was followed by about 10 years as a project biometrician at Chiron-Behring in Marburg, where he headed the biometry, and at Schering AG. After this, he worked as project leader Specialized Therapeutics in the department of Global Health Economics and Outcomes Research at Bayer-Schering Pharma AG in Berlin.

Dr. Schwenke founded SCO:SSiS in 2007. Main areas of work are clinical development and – particularly since introduction of the AMNOG in 2011 – the area of market access and benefit assessment. A list of publications can be found in Medline (http://www.ncbi.nlm.nih.gov/pubmed/?term=Schwenke+C). 

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01 January 2018

Meta-analysis combines the results from two or more studies. If used appropriately, it is a powerful tool to summarize results from multiple studies, provides insights into heterogeneous studies, and assists in deriving meaningful conclusions. This course will take you through the steps involved in conducting a meta-analysis.

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01 January 2018

Steps in conducting a meta-analysis.

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01 January 2018

Choice of effect measures and model in meta-analysis.

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01 January 2018

Graphics and software for meta-analysis.

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01 January 2018

Fixed-effect approaches for meta-analysis.

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01 January 2018

Random-effects approaches for meta-analysis.

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01 January 2018

Brief introduction to network meta-analysis and Bayesian methods.

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01 January 2018

Conclusion.

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01 January 2018

Introduction to meta-analysis.

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12 December 2017

In traditional PK/PD trials, pharmacokinetics (PK) is investigated in the satellite group of animals, and the pharmacodynamics (PD) is investigated in the study group of animals. The new blood sampling method of microsampling opens up the opportunity to investigate both PK and PD in the same animals. To avoid excessive burden on the animals from the required blood sampling, sparse sampling schemes are typically utilized. Motivated by this application, this talk introduces a procedure to choose an optimal sparse sampling scheme and sampling time points using non-compartmental methods but which can be applied to further settings beyond this. We discuss how robust designs can be obtained and we apply and evaluate the approach to a range of scenarios to give an example of how it may be implemented. The results are compared to optimal designs for model based PK.

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17 November 2017

Under EMA Policy 70 Publication of Clinical Data, clinical overviews and study reports submitted as part of a Marketing Authorisation Application are published and made publically available. Before documents are published, sponsors need to anonymise the documents aligned to data privacy requirements.

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The intent of this webinar is to describe what is involved in anonymising clinical data, and present the different methods and approaches available for anonymisation including an introduction to residual risk assessment.  Examples from the PhUSE Data De-Identification Standard will also be discussed.

Jean-Marc Ferran (Qualiance / PhUSE) will present ‘Anonymising Clinical Data – key principles, methods and considerations’.  Chrissie Fletcher will facilitate a Q&A session.

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02 November 2017

Defining the estimand of interest in a clinical trial is crucial to align its planning, design, conduct, analysis, and interpretation. The need for more precise specifications of estimands is highlighted in the draft addendum ICH E9(R1) which was published for public consultation in August 2017. Although not explicitly mentioned in ICH E9(R1), the addendum brings causal reasoning – besides randomization and ITT – into our world of pharmaceutical statistics. In this webinar, we will discuss the link between the ICH E9(R1) and causal inference. Furthermore, per protocol analyses will be discussed from a causal inference perspective and a case study where a principal strata estimand was investigated will be presented.

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Using principal stratification to address post-randomization events: A case study

Baldur Magnusson, Novartis Pharma AG

In a randomized controlled trial, occurrence of post-randomization events associated with treatment and the primary endpoint may complicate the interpretation of the overall treatment effect. In this presentation, we discuss how these events may be accounted for at the estimand and the estimator level in the context of a recent case study. We define a principal stratification estimand derived from the scientific question of interest. Consideration is given to identifying assumptions, model-based derivation of an estimator, handling of covariates and missing data. We also discuss the role of sensitivity analyses.

Estimands and Causal Inference

Daniel Scharfstein, Professor of Biostatistics, Johns Hopkins Bloomberg School of Public Health

Recently, the ICH proposed an addendum to the E9 Guidance: Statistical Principles for Clinical Trials. This addendum is focused on estimands and sensitivity analysis for randomized trials with intercurrent events. In this webinar, I will discuss the potential outcomes framework for causal inference and use it to formally define estimands that address different types of intercurrent events. I will then discuss the assumptions required to identify these estimands from the observable data and discuss the important role of sensitivity analysis. 


Estimating Causal Effects in Clinical Endpoint Bioequivalence Studies in the Presence of Treatment Noncompliance and Missing Data

Wanjie Sun, FDA/CDER/OB/DBVIII

In clinical endpoint bioequivalence (BE) studies, the primary analysis for assessing equivalence between a generic and an innovator product is usually based on the observed per-protocol (PP) population (i.e., completers and compliers in general). The FDA Missing Data Working Group and the ICH E9 Revision 1 Working Group recommended using “causal estimands of primary interest.” The analysis based on the PP population, however, is not generally causal because PP is determined post-treatment, hence conditioning on it may introduce selection bias. To date, no causal inference has been proposed to assess to equivalence. In this paper, we propose a causal framework and co-primary causal estimands to test equivalence by applying Frangakis and Rubin (2002)’s principal stratification in causal inference. We identify three conditions when the current PP estimator is unbiased for one of the proposed co-primary causal estimands – the“Survivor Average Causal Effect” (SACE) estimand. Simulation was used to demonstrate the bias, type 1 error, and power associated with the PP estimator when these three conditions are not met. We also propose a tipping point sensitivity analysis to evaluate the robustness of the current PP estimator (primary analysis) in testing equivalence when the underlying sensitivity parameters vary across a clinically meaningful range. Data from a clinical endpoint BE study is used to illustrate the proposed co-primary causal estimands and sensitivity analysis method. Our work starts causal evaluation of equivalence assessment in clinical endpoint BE studies with non-compliance and missing data, and can be applied to clinical biosimilar and non-inferiority studies.

*The views expressed in this article represent the opinions of the authors, and do not represent the views and/or policies of the U.S. Food and Drug Administration.

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30 October 2017

In this webinar, the EU regulatory and Industry members of the ICH E9(R1) working group will present the new draft addendum for ICH E9 on estimands and sensitivity analysis. The addendum introduces a new framework for designing and analysing clinical trials aligned to the trial objectives.

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Rob Hemmings (MHRA) will present the motivation behind the new draft addendum, define estimands and sensitivity analysis, and explain different strategies that can be used in constructing an estimand.

Frank Bretz (Novartis) will present case studies to illustrate how the new framework can be implemented in designing clinical trials and defining the appropriate analysis methods.

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23 October 2017

In questions like “How much more efficacy is needed to outweight tolerability issues?” preferences by patients or other stakeholders could play a key role in quantitative benefit-risk assessments.

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Abstract

Both regulators and payers are actively exploring how they might use quantitative estimates of patient preferences to support their decisions. This is evident in initiatives such as IMI PREFER.

Does this development provide the possibility of greater alignment in the data requirements of these decision makers? While it still poses methodological challenges, the legitimacy of using patient preferences data to support regulatory decisions is easier to establish.

The societal level resource allocation involved in reimbursement decisions causes us to question the role of the patient. When issues such as the equity of resource allocation between patients are involved, what role can patients’ preference play?

This webinar will explore the role of patient preferences in reimbursement decisions, both current practice and the future potential. Starting from a quantitative benefit risk assessment (BRA) to inform a regulatory decisions, we will incrementally consider the broader set of factors relevant to reimbursement decisions – alternative comparator treatments, costs, and equity considerations – to explore whether and how a  BRA might play a role in reimbursement decisions. In doing so, we will explore current practice in incorporating stakeholders (patients and others) into reimbursement decisions, and how this differs depending on the decision problem posed by payers.

Finally, we will conclude by proposing roles for patient preferences in reimbursement, and the research agenda required to determine the usefulness and feasibility of these proposal.

About the Presenter

Kevin Marsh, PhD, is an expert in the use of preference information and decision analysis to inform health decisions, including pipeline optimisation, authorisation, reimbursement and prescription decisions, Kevin’s research interests include preference elicitation, decision modelling, and MCDA. He actively contributes to the methodological development of these techniques. Kevin currently co-chairs the ISPOR Taskforce on the use of MCDA in Health Care Decision-Making. He has applied these and other research techniques for a range of organisations, including both regulatory and industry clients. The former has included a range of UK-based, health-related organisations, such as the National Institute for Health and Care Excellence, the National Institute for Health Research, and the Department of Health.

Kevin completed his PhD at the University of Bath, specialising in economic valuation techniques. After a year at Oxford University, he joined the Matrix Knowledge Group in London, where he built their economics practice. Kevin is an active member of the Campbell and Cochrane Economic Methods Group and contributes to methodological development in the field of economic evaluation.

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26 September 2017

Chrissie Fletcher, Amgen - The new addendum to ICH E9 on estimands and sensitivity analyses introduces a new framework for clinical trial design, conduct, analysis and interpretation of results. In the new framework the first step is to ensure there is a clearly defined clinical trial objective. The trial objective will lead to defining the estimand, the treatment effect to be estimated, which will influence the choice of trial design. The estimand will lead to defining appropriate statistical analyses to derive estimates of treatment effects, including sensitivity analyses that are aligned to the estimand. The new framework in the ICH E9 addendum will enable sponsors to discuss with regulators prior to the clinical trial commencing what estimand is of primary interest. Choices made in the study design and planned statistical analyses describing how intercurrent events, such as non-adherence, use of rescue medication, and deaths occurring in the study, will be handled can impact what treatment effect is actually being estimated in a clinical trial. Therefore alignment in the choice of estimand and planned statistical analyses, including sensitivity analyses, will improve the interpretation and understanding of trial results. This presentation will provide an overview of the new addendum including examples illustrating how to use the new framework in designing clinical trials.

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26 September 2017

Christoph Gerlinger, Bayer - The forthcoming addendum to the ICH E9 guidance on statistical principles for clinical trials has major implications on almost all aspects of drug development trials. Despite being a multidisciplinary topic it is often perceived as an exclusively statistical topic in adjacent functions like medical, project management, or regulatory affairs. A broad working group of statisticians interested in the estimands framework was founded in our company early 2016 to prepare not only the statisticians but also the whole company for the changes in the way we plan, run, and analyze clinical trials in the future. This talk will review the actions taken before the release of the ICH draft addendum: Creation of a white paper, summaries of the key publications, and two pilot workshops all aimed mainly at clinicians in drug development. We will also discuss our plans to roll out the estimands concept both within the statistics department and also to the whole company once the draft addendum is published.

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26 September 2017

Ann-Kristin Leuchs, BfArM - The precise definition of the estimand of primary interest (treatment effect to be estimated) with regard to specification of handling intercurrent events such as recue medication or non-adherence is essential when planning and designing randomized controlled trials (RCT). The analysis should then be aligned to the agreed primary estimand. In this context the question arises how this fits in with the intention-to-treat (ITT) principle. Although the ITT principle has long-since been the gold standard of analyzing RCTs. Despite this there was and still is much ambiguity involved around what is considered to constitute ITT, especially in relation to the problem of missing data and, in recent years, also with regard to intercurrent events and estimands. While some argue it is simply analyzing all patients as randomized, others regard ITT as addressing the treatment policy estimand. This talk focuses on the author’s thinking on the ITT principle and its definition, on how it is distinct from the missing data and estimand problem and on how to best move forward. Since ICH E9 is imprecise concerning the ITT principle, discussing an addendum to ICH E9 might be an ideal time point to solve the confusion and ambiguity around defining it. The ITT principle could well remain gold standard of analyzing RCTs even while allowing various different estimands to conform to it.

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26 September 2017

Yolanda Barbachano, MHRA - Though the Addendum to ICH E9 was clearly motivated by a need to more precisely define the measure of treatment effect in clinical trials, in the context of efficacy, the same framework is also applicable and helpful when thinking about how to collect and present the safety data. Furthermore, we should ideally think about the estimand of interest for each endpoint or trial objective separately, regardless of whether they are primary or secondary, hypothesis testing or descriptive. In this talk I will move away from the usual discussion around the choice of estimand for the primary efficacy endpoint, and instead present some examples on safety, tolerability and quality of life, to illustrate the value of the estimand framework in a wider context. ​

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26 September 2017

Francesca Callegari, Novartis - An estimand clearly defines the treatment effect to be estimated in a clinical trial. An ICH E9 addendum is under preparation, which will introduce the concept of estimand and will provide a structured framework to link trial objectives of a clinical trial and statistical methods in a coherent way. In the meantime, regulators are keen to know the definition of estimands for new clinical trials. In this presentation, we focus on a Phase 2 study in chronic pain. The definition of the primary estimand in this context takes into account relevant post-randomization events, which are often informative of the treatment effect of interest, such as intake of concomitant medications and premature discontinuations of study treatment. Other supplementary and secondary estimands are also defined to assess the treatment effect under different handling of the post-randomization events or under different specifications of the variable of interest. Some practical considerations coming from the development of the estimand concept for this trial from its inception till its detailed specification are summarized, outlining the challenges encountered and how these have been overcome.

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26 September 2017

Martin Jenkins, AstraZeneca - In many chronic, systemic diseases the goal of treatment is to manage patient symptoms and to prevent disease flares. The effect of current therapies is generally reversible and as such it is not necessarily of primary interest to address a treatment policy estimand, but rather to consider the effects attributable to the initially randomised treatment. In addition, defined treatment pathways mean that it is common that estimands in this area must consider the handling of patients who use rescue treatments or escalate therapy. Drawing on examples in rheumatology, dermatology, autoimmune and respiratory disease areas I will compare different scenarios to describe how the precise choice of estimand should take into account the type of endpoint, current treatment paradigm and any retention of treatment effect upon discontinuation.

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26 September 2017

Chris Harbron, Roche - Observed time-to-event endpoints typically contain many censored observations. As a consequence, many of the standard analysis approaches e.g. Kaplan-Meier and Proportional Hazards are specifically designed to address these partially missing data. This ability to cope with data being missing due to censoring has frequently led to the benefits of estimands for addressing other types of intercurrent events being overlooked. In this presentation I will discuss how the estimand framework provides a vehicle for explicitly describing and addressing several of the challenges within time-to-event analyses such as treatment cross-over, informative censoring, lack of blinding and inconsistent definition of endpoints.

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25 September 2017

Recently, we shared an example of adopting benefit risk methodology to schizophrenia studies. Now we are happy to announce that Eva Katz, the epidemiologist behind this case study, will give a webinar about it and discuss how other areas can benefit.

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Eva Katz, PhD, MPH, RD is Associate Director of Benefit-Risk and Epidemiology at Janssen Research & Development.  In this role Dr. Katz serves as an internal consultant for benefit‐risk methodology and patient‐focused benefit‐risk assessment, guiding clinical teams across therapeutic areas in medication benefit-risk assessment using both qualitative and quantitative methods.  As part of the efforts to integrate the patient experience in clinical trials, Dr. Katz has helped integrate patient and physician preference surveys within phase 3 clinical trials. Eva also participates in external task forces on benefit‐risk assessment methods and patient focused drug development. Prior to her role in Benefit-Risk, Dr. Katz was part of the patient reported outcomes team at Janssen where she worked cross‐functionally to develop strategy for development, selection and implementation of Patient‐Reported Outcomes (PROs) in phase 2 and phase 3 clinical trials of pharmaceutical products across therapeutic areas. Dr. Katz received her B.S. in Nutritional Science from Rutgers University, her M.P.H. from the University of California, Berkeley and her doctorate in Nutrition Epidemiology from the Gillings School of Global Public Health, University of North Carolina, Chapel Hill.

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18 September 2017

The bacterial reverse mutation assay (or the Ames test) has been in use in its present form for over 40 years. It is arguably the most successful in vitro test, used by hundreds of laboratories worldwide, on thousands of substances. The test aims to identify substances that can produce genetic damage and may lead to cancer in exposed individuals or to inherited mutation in offspring to cancer.

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The design of the test is basically simple and numerous statistical tests have been proposed for the analysis of the data produced. Interpretation of the result aims at categorizing the chemical as either a genotoxin or a non-genotoxin. This provides an interesting example of the contrast between statistical significance and biological interpretation. Ames test results are also used in helping to develop in silico methods for predicting carcinogenicity.

This presentation will illustrate these issues and also discuss newer versions of the test and the continuing assessment of the role of the test in toxicology.

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11 July 2017

In this 1 hour webinar we will have a presentation from Conny Berlin who is the industry project leader of the public-private IMI PREFER project and Rachael DiSantostefano, a task co-leader on the IMI PREFER project.

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Abstract

The main objective of the IMI PREFER project is to strengthen patient-centric decision making throughout the life cycle of medicinal products by developing evidence-based recommendations to guide industry, Regulatory Authorities, HTA bodies, reimbursement agencies, academia, and health care professionals on how and when patient-preference studies should be performed and the results used to support and inform decision making.

While over the last years all stakeholders gained experience individually how to engage patients for decision making this project aims to bring all stakeholders together taking a structured approach to determine their needs, expectations, and concerns regarding the use of patient-preference information and methodologies for patient-preference elicitation. 

Methodologies for patient value elicitation are available and have been used frequently in market research, in health economics and outcomes research to substantiate real-life evidence. Further structured research has been done in projects like IMI PROTECT but there is no systematic use of these methodologies in the regulatory licensing processes yet. 

The presentation will address 

  • Objective of PREFER
  • Changing environment
  • Patient preference study example
  • PREFER participants
  • PREFER project approach & status

Conny Berlin, Global Head Quantitative Safety & Epidemiology, Novartis International AG

Conny Berlin leads the Quantitative Safety & Epidemiology group at Novartis International AG. She holds a degree in mathematics from the University of Rostock, Germany and has more than 25 years of experience within the pharmaceutical industry.

Conny Berlin has a profound knowledge of quantitative methodology as applied to clinical and observational data and to spontaneous reports to respond to safety and benefit-risk questions during drug development and post-approval.

Conny Berlin is a member of the company’s internal Medical Safety Review Board and of the Real World Evidence Leadership Team.

She is well experienced in managing projects, leading and coordinating interdisciplinary teams. Conny Berlin is the industry project leader of the public-private IMI PREFER project.

 Relevant references 

  • Esther W. de Bekker-Grob, Conny Berlin et al. Giving Patients’ Preferences a Voice in Medical Treatment Life Cycle: The PREFER Public–Private Project. Patient: Editorial
  • Participant of the CIOMS working group X on "Evidence Synthesis and Meta-Analysis for Drug Safety"; report published in 2016
  • Berlin C, Blanch C et al. Are all quantitative postmarketing signal detection methods equal? Performance characteristics of logistic regression and Multi-item Gamma Poisson Shrinker. Pharmacoepidemiology and Drug Safety. 2011: 622-630


Rachael DiSantostefano, PhD, MS is a Director, Benefit-Risk in Epidemiology at Janssen R&D

Rachael L. DiSantostefano has nearly 25 years of pharmaceutical research experience across the quantitative disciplines of epidemiology, biostatistics, and health outcomes.  She is currently responsible for guiding clinical teams in structured benefit-risk assessment, including the use of both qualitative and quantitative methods.   She received her PhD in Health Policy and her Master’s degree in Biostatistics at the University of North Carolina Gillings School of Global Public Health.  Prior to joining Janssen in 2015, she was an epidemiologist at another pharmaceutical company for 10 years, where she evaluated medication safety and contributed to benefit-risk assessment in regulatory submissions and at FDA Advisory Committee meetings.  She is currently active as a task co-leader on the IMI PREFER project and an active member of the Benefit-Risk Assessment Communication and Evaluation Special Interest Group (BRACE-SIG) within the International Society for Pharmacoepidemiology.

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11 July 2017

At this joint PSI/RSS journal club webinar, held on 12th July 2017, we heard two excellent presentations on the topic of Adaptive Signature Designs, followed by a fascinating discussion led by Dr Richard Simon, who founded the original methodology. Dr Richard Simon recently retired as associate director of the Division of Cancer Treatment & Diagnosis and Chief of the Computational and Systems Biology Branch at the National Cancer Institute, Maryland, USA. The meeting was chaired by Steve Gilmour, Professor of Statistics at King’s College London.

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Our speakers were: 

Dr Zhiwei Zhang, Associate Professor of Biostatistics at the University of California, Riverside, who authored 'Subgroup Selection in Adaptive Signature Designs of Confirmatory Clinical Trials', published February 2017 in JRSS Series C, Volume 2.  Co-authored by Meijuan Li, Min Lin, Guoxing Soon, Tom Greene and Changyu Shen.  Please click here to view the slides. 

Dr Gu Mi, Research Scientist at Eli Lilly and Company in Indianapolis, Indiana, who authored 'Enhancement of the adaptive signature design for learning and confirming in a single pivotal trial' published May 2017 in Pharmaceutical Statistics.

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20 June 2017

Data sharing, data transparency and data privacy are areas that continue to evolve. For example, EMA Policy 70 is now effective with proactive publication of submitted clinical overviews and study reports. It is becoming more common for clinical trial data to be used to support scientific questions beyond the objectives of the original study.

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The intent of this webinar is to provide an outline to pharmaceutical statisticians of key issues and concepts everyone dealing with and sharing patient level data should be aware of.

Presentation #1 Sharing clinical trial data externally – key data privacy concepts every statistician should know (Janice Branson and Nicola Orlandi (Novartis))

Presentation #2 Data Sources to help inform drug development – what you give is also what you get (Sally Hollis (Phastar) and Rebecca Sudlow (Roche))

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12 June 2017

Patients have long been an important part of clinical drug development – without them, there would be no new medicines. Recently, there has been a fundamental shift in their involvement in the drug development process. Today, patients are highly active in engaging in discussions about their disease, what they look for in new treatments, and how clinical trials are designed and conducted.

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Following on from the successful and thought-provoking presentation at last year’s PSI conference by Paul Wicks from PatientsLikeMe, this webinar will continue to explore the ways in which patients are influencing the design of new clinical trials.  We have two speakers who bring different experience and perspectives on this subject:

Patient-centric medicines development – the value of online health communities

Dr Cathy Emmas, Partnership Director, Patient Centricity, AstraZeneca

Abstract: How AstraZeneca’s collaboration with the PatientsLikeMe is accelerating our ability to generate the timely and relevant patient insight that enables informed decision making within our R&D programs. What patient-generated health data tells us about symptoms and outcomes that matter to patients. Optimisation of clinical trials from the patient perspective.

Biography: Cathy is the Partnership Director in AstraZeneca’s global Patient Centricity team where she leads a 5 year strategic collaboration with the PatientsLikeMe online health network. This alliance was established to accelerate our ability to generate the right patient insight that enables informed decision making within our R&D programs and shape healthcare delivery. In the first two years the collaboration has linked the experiences of over 70,000 patients into our lifecycle teams and patient preferences have helped shape 12 clinical studies across 7 diseases. 

Achieving Effective Patient Public Involvement in Clinical Trials: “No research about us without us”

Professor Sue Pavitt, Dental Translational and Clinical Research Unit, University of Leeds

Abstract: Patient public involvement in medicine research and development has gained significant momentum. Adopting a patient-centric approach in clinical trials and research is important to ensure new treatments embrace what is important to patients. Achieving effective Patient Public Involvement & Engagement (PPIE ) partnerships enhances the relevance of clinical research and improved likelihood of delivering patient benefits. PPI also contributes to the operational efficiency and success in clinical trial design, ethical approval, conduct and dissemination reach; collectively building cross sector communication and partnerships may enhance market head room long term. I will provide a background to patient public involvement and establishing effective partnerships and illustrative case examples that support patient awareness of their treatment options and healthcare choices. I will introduce EUPATI and its role in meeting the educational needs to deliver patient centric medicine R&D and facilitate partnerships between patients-academia and industry in clinical research. We are in an era of a paradigm shift in patient-centric clinical trials, by strategically bringing the patient lived experience to the forefront has the potential to change fundamentally how health care is practiced.

Biography: Sue Pavitt - Prof in Translational & Applied Health Research, University of Leeds. Her PhD was in Human Cancer Genetics and she had a high profile career working with Prof Sir Walter Bodmer mapping the first colorectal cancer gene. She worked on the Human Genome Project at UCL, Oxford and UCSF, USA. In 1998 she was appointed as the Founding Director of TayRen – the premier Scottish multidisciplinary Primary Care Research network and the academic focus of her career changed to applied health research. She became the Divisional Director at the Clinical Trials Research Unit, Leeds and has since gone on to Head the Division for Applied Health and Clinical Translation and is Director of the Dental Translation and Clinical Research Unit. She is a Specialty lead for the Oral & Dental Health National Institute of Health Research (NIHR) Clinical Research Network. She is Chair of Multiple Sclerosis Clinical Trials Network. She is a Board Member of the MRC-NIHR Efficacy & Mechanistic Evaluation and in this capacity evaluated clinical trial design. Her research portfolio spans several disease areas and is characterized by forging effective, multi-disciplinary research partnerships between clinicians, academics, sometimes industry and always patients; developing methodological sound projects that are patient-centric with research questions tailored to clinical priorities to maximize impact and patient benefit. Sue is passionate about patient involvement in research with >30 years’ experience. She is the Academic Lead for EUPATI-UK – European Patient Advocacy for Therapeutic Innovation- a pan-European Innovative Medicines Initiative, led by the European Patients' Forum, in partnership with patient organizations, universities, not-for-profit organizations and pharmaceutical companies. EUPATI’s goal is to increase capacities and capabilities of well-informed patients to be effective advocates/advisors in medicines research.

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03 May 2017

Personalised medicines - which are designed to treat particular groups of patients - are becoming increasingly prominent. In order to identify patients suitable for treatment a companion diagnostic assay is often needed. The Personalized Medicines Coalition (PMC) recently published an article stating that 25% of NME approved by FDA in 2016 included a companion diagnostic.

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This webinar will introduce and examine some of the considerations required for statisticians working in the field of companion diagnostics and will include information from an FDA (CDRH) speaker and perspectives/ case-studies from representatives from both a pharmaceutical company and a diagnostic company.

Speakers:

Meijuan Li, CDRH
Peter Cooper, Qiagen
Rachel Hodge, AstraZeneca

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22 March 2017

Please join us to hear Jing Huang (Veracyte Inc.) and Tarylee Reddy (South African Medical Research Council) present their recent work from Pharmaceutical Statistics:

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Speaker: Hui Yang (Amgen)
A visualization method measuring the performance of biomarkers for guiding treatment decisions 
Authors: Hui Yang, Rui Tang, Mike Hale and Jing Huang
Pharmaceutical Statistics, Volume 15, Issue 2, Pages 152-164, March/April 2016 

Speaker: Tarylee Reddy (South African Medical Research Council)
A novel approach to estimation of the time to biomarker threshold: applications to HIV
Authors: Tarylee Reddy, Geert Molenberghs, Edmund Njeru Njagi, Marc Aerts
Pharmaceutical Statistics, Volume 15, Issue 6, Pages 541-549, November/December 2016

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09 February 2017

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The Emerging and Merging Fields of Benefit-Risk and Health Technology Assessments

Jason (Jixian Wang), Shahrul Mt-Isa and Susan Talbot, on behalf of the EFSPI BRA/HTA joint working group

Abstract: Benefit-risk assessments (BRA) focus on clinical aspects of health care products and are often seen as purely regulatory activities, while health technology assessments (HTA) consider a wider range of aspects, but mainly concentrate on economic evaluations. Despite different objectives, the perspectives and requirements of the two domains are becoming more in sync than a decade ago. This is evidenced by the formations of various initiatives to address novel challenges, raising the bar for those directly involved in providing justifiable evidence for decision-making on health technologies for the good of public health. With increasing methodological demands and considerations that are no longer unique to HTA or BRA in regulatory submissions, more issues have surfaced and more questions have been raised. Despite the numerous efforts, the recommendations remain diverse and the efforts remain distinct. The EFSPI/PSI joint working group for BRA and HTA has conducted an extensive review of the initiatives and investigated methodologies to recommend practical approaches to improve HTA with an integrated BRA. We will present an up-to-date review of the outputs from key initiatives focusing on methodologies, and will compare approaches taken by HTA authorities with those taken by the regulatory agencies. 

About the presenter: Jason (Jixian) Wang is a principle statistician at Celgene, with over 25 years of experiences as statistician in a number of areas in pharmaceutical statistics, has published more than 50 peer reviewed papers and a book on exposure-response modeling. He worked on health economics and outcome researches and epidemiology in academic institutes for several years before moving to industry positions supporting clinical pharmacology in Phase I-III trials and regulatory submissions, with a number of successful NDA submissions to the FDA/EMEA . Since 2014, he has been working on health economics and outcomes researches to support global market access. His current interests are on health economics modeling, real world evidence generation and causal inference, and structured benefit-risk and health technology assessments. He is a member of PSI special interest groups for real world data (formally epidemiology), modeling and simulation and health technology assessment (HTA). He is leading a working group on clinical trial extrapolation for HTA, and is a coordinator for the EFSPI joint working group for benefit-risk assessment and HTA.  

Benefit-Risk Assessment via Case Studies: Key Considerations and Best Practices

Abstract: The development and implementation of benefit-risk assessment is multi-faceted and should be done throughout the clinical development life cycle. Use of structured benefit-risk framework could enhance regulatory decisions, both in terms of scientific validity and in terms of consistency and transparency to stakeholders. In this talk, we describe two real examples that regulatory agencies considered in benefit-risk evaluations, resulting in different outcomes in their approval and marketing status. These case studies illustrate a few key considerations (i.e subgroup identifications, endpoint selection with important clinical impacts, uncertainty quantification, risk mitigation etc.) for a full benefit-risk evaluation. 

About the presenter: Dr George Quartey is a Strategic Innovation Leader for Safety Risk Management at Roche-Genentech with over 25 years of diverse experience in statistical research, risk-benefit modeling, comparative effectiveness research, evidence synthesis and data Mining. He is currently responsible for leading major innovation and enablement in areas relating to Benefit-Risk Assessment of Medicines, Machine Learning and Predictive Safety Monitoring as well as Safety Strategies for Handling HTA. Dr Quartey published and spoke widely on both theoretical and pragmatic aspects of benefit-risk assessment of medicines and served on several internal and external committees that inform policy on benefit-risk and quantitative safety methods including IMI PROTECT, QSPI Benefit-Risk Working Group and CIOMS X working group on "Evidence Synthesis and Meta-Analysis for Drug Safety". Dr Quartey is currently the co-director of the IMI EU2P program on benefit-risk assessment of medicines.

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30 January 2017

Drug development is becoming more expensive and more risky and so getting your data and decisions right first time is becoming more valuable. This presentation describes the crucial contribution that statistical insight makes to getting it right first time.

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16 November 2016

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John Scott is Deputy Director of the Division of Biostatistics in the FDA's Center for Biologics Evaluation and Research, where he has also served as a statistical reviewer for blood products and for cellular, tissue and gene therapies. Prior to joining the FDA in 2008, he worked in psychiatric clinical trials at the University of Pittsburgh Medical Center and did neuroimaging research with the Neurostatistics Laboratory at McClean Hospital, Harvard Medical School. He has authored or co-authored numerous articles in areas including Bayesian and adaptive clinical trial design and analysis, drug and vaccine safety, data and text mining, and benefit-risk assessment. He holds a Ph.D. in Biostatistics from the University of Pittsburgh and an M.A. in Mathematics from Washington University in St. Louis, and is an associate editor of the journal, Pharmaceutical Statistics.

U.S. regulatory considerations and case studies for rare diseases
In this talk, I will present an overview of the U.S. Food and Drug Administration’s policies and practices for encouraging development of products for rare diseases and of evaluating clinical evidence for the safety and effectiveness of such products. I’ll discuss study designs that may be particularly appropriate for rare disease product development, and address some of their statistical implications. Finally, I’ll present case studies of products that were approved for rare diseases using unusual or innovative study designs and/or regulatory pathways.

Lisa Hampson is a Lecturer in Statistics at Lancaster University. Her research interests are in clinical trials, including group sequential tests and Bayesian methods for trials in rare diseases and dose-escalation. Her recent research has focused on developing methods for clinical trials of new medicines for children. She holds a PhD in Statistics from the University of Bath. 

Bayesian methods for the design and interpretation of clinical trials in rare diseases
For studies in rare diseases, the sample size needed to meet a conventional frequentist power requirement can be daunting, even if patients are to be recruited over several years. Rather, the expectation of any such trial has to be limited to the generation of an improved understanding of treatment options. We propose Bayesian approaches for the conduct of rare disease trials comparing an experimental treatment with a control when the primary endpoint is binary or normally distributed. We describe processes which can be used to systematically elicit from clinicians opinions on treatment efficacy in order to establish Bayesian priors for unknown model parameters. The proposed approaches are illustrated by describing applications to two Bayesian randomised controlled trials, namely a study in childhood polyarteritis nodosa and a study in chronic recurrent multifocal osteomyelitis. Once prior distributions have been established, consideration of the extent to which opinion can be changed, even by the best feasible design, can help to determine whether a small trial is worthwhile.

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08 May 2016

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The Statistical Evaluation Of Surrogate Endpoints In Clinical Trials

Geert Molenburghs  (I-Biostat)

Both humanitarian and commercial considerations have spurred intensive search for methods to reduce the time and cost required to develop new therapies. The identification and use of surrogate endpoints is a general strategy that has stimulated much enthusiasm, but how can one establish the adequacy of a surrogate, in the sense that treatment effectiveness on the surrogate will accurately predict treatment effect on the intended, and more important, true outcome? What kind of evidence is needed, and what statistical methods portray that evidence most appropriately? The definition of validity, as well as formal sets of criteria, have been proposed, including use of the proportion explained, jointly the within-treatment partial association of true and surrogate responses, and the treatment effect on the surrogate relative to that on the true outcome.  In a multi-centre setting, these quantities can be generalized to individual-level and trial-level measures of surrogacy. Consequently, a meta-analytic framework studying surrogacy at both the trial and individual-patient levels has been proposed. The framework commonly used will be sketched, also against the background of alternatives. A perspective will be given on further and ongoing developments.

A Surrogate Endpoint For Chronic Lymphocytic Leukemia

Natalie Dimier (Roche)

The standard primary endpoint in clinical trials of chronic lymphocytic leukemia (CLL) is progression-free survival (PFS). Patients with CLL who achieve levels of minimal residual disease (MRD) of <1 clonal cell/10,000 leukocytes in peripheral blood (PB) at the end of initial treatment are considered MRD negative, and have been shown to experience significantly improved PFS. This analysis aims to support the evaluation of MRD response at the end of treatment as a surrogate endpoint for PFS in CLL, based on a retrospective analysis of 3 multicenter, randomized, Phase 3 clinical trials containing a total of 1203 patients. The primary endpoint of each study was investigator-assessed PFS and a meta-regression model was developed to predict treatment effect on PFS using treatment effect on MRD.

Overall Response Rate, Progression-Free Survival, And Overall Survival With Targeted And Standard Therapies In Advanced Non–Small-Cell Lung Cancer

Hui Zhang, Shenghui Tang  (FDA)

We conducted analyses to explore trial-level and patient-level associations between overall response rate (ORR), progression-free survival (PFS), and overall survival (OS) in advanced non-small-cell lung cancer (NSCLC) trials. We identified 14 trials (N = 12,567) submitted to US Food and Drug Administration since 2003 of treatments for advanced NSCLC. Only randomized, active-controlled trials with more than 150 patients were included. Associations between trial-level PFS hazard ratio (HR), OS HR, and ORR odds ratio were analyzed using a weighted linear regression model. Patient-level responder analyses comparing PFS and OS between patients with and without an objective response were performed using pooled data from all studies. On a trial level, there is a strong association between ORR and PFS. An association between ORR and OS and between PFS and OS was not established. The patient-level analysis showed that responders have a better PFS and OS compared with nonresponders.

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10 March 2016

This Journal Club was on the topic of Poisson/Negative Binomial Modelling and the different techniques for dealing with missing data, with speakers Richard Kay and Mouna Akacha presenting their work.

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Speaker: Richard Kay (RK Statistics)

The analysis of incontinence episodes and other count data in patients with overactive bladder by Poisson and negative binomial regression
Authors: R Martina, R Kay, R van Maanen, A Ridder
Pharmaceutical Statistics, Volume 14, Issue 2, Pages 151-160, March/April 2015

Speaker: Mouna Akacha (Novartis)

Sensitivity analyses for partially observed recurrent event data
Authors: Mouna Akacha and Emmanuel O. Ogundimu
Pharmaceutical Statistics, Volume 15, Issue 1, Pages 4-14, January/February 2016

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16 September 2015

This Journal Club was on the separate topics of Simulation and Hypothesis Testing. We were pleased to have speakers Andrew Grieve and Anne Benoit join us to present their work published in Pharmaceutical Statistics.

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Speaker: Andrew P. Grieve (ICON Adaptive Trials Innovation Centre)

How to test hypotheses if you must
Author: Andrew P. Grieve
Pharmaceutical Statistics, Volume 14, Issue 2, Pages 139-150, March/April 2015#

Speaker: Anne Benoit (Université catholique de Louvain and GSK Biologicals)

Influenza vaccine efficacy trials: a simulation approach to understand failures from the past
Authors: Anne Benoit, Catherine Legrand and Walthère Dewé
Pharmaceutical Statistics, Volume 14, Issue 4, Pages 294-301, July/August 2015

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12 December 2013

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Chair: Alun Bedding (Roche)

Speakers
: Harry Southworth (Data Clarity Consulting Limited) and Janet Heffernan (J. Heffernan Consulting) 


Extreme value modelling of laboratory safety data from clinical studies
Authors: Harry Southworth and Janet E. Heffernan; 
Pharmaceutical Statistics, Volume 11, Issue 5, Pages 361–366, September/October 2012 


Multivariate extreme value modelling of laboratory safety data from clinical studies
Authors: Harry Southworth and Janet E. Heffernan; 
Pharmaceutical Statistics, Volume 11, Issue 5, Pages 367–372, September/October 2012

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29 October 2013

Joint PSI Pharmaceutical Statistics & DIA Statistics Community.

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DIA Chair: Tad Archambault, Principal, Virtu Stat, Ltd.

Speaker: Daniel Wachtlin, Interdisciplinary Centre for Clinical Trials (IZKS), University Medical Centre Mainz

Blinded Sample Size Recalculation in Longitudinal Clinical Trials Using Generalized Estimating Equations
Daniel Wachtlin and Meinhard Kieser 
Therapeutic Innovation & Regulatory Science, Vol. 47, Issue 4, pp. 460-467, 2013 

PSI Chair: Byron Jones, NOVARTIS

Speaker: Andrew Hartley, PPD

Adaptive blinded sample size adjustment for comparing two normal means—a mostly Bayesian approach
Andrew M. Hartley 
Pharmaceutical Statistics, Vol. 11, Issue 3, pp. 230-240, 2012 

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