\n Sar ah Bö\;hme (Pfizer) \;
\n David Phillippo (University Bristol)
\n
\n Yann Ruff ieux (University Bern)
\n \ n
Alan Brnabic (Lilly)
\n\n Mark Belger (Lilly)
\n \n
Keith Abrams (University Leicester)
\n < /td>\nBreak out #1: Bucher vs matching adjusted indirect co mparisons and further refinements of these
\nBre ak out #2: Generalizability of clinical trial data into real life settings
\nBreak out #3: Cross-design approaches combining observational and clinical trial data
\nPanel discussion
\nAlexander
Schacht (Lilly\, moderator)
\n Carsten Schwenke
(SCOSSIS)
\n Ralf Bender (IQWiG)
\n
Nicky Welton (University Bristol)
\n
Keith Abrams (University Leicester)
\n Chrissie F
letcher (Amgen)
\n Mark Belger (Lilly)
\n
\n
\n Indirect comparisons with and without adjustment for patient characteristics and related approaches - \;Sarah Bö\; hme (Pfizer)\, David Phillippo (University Bristol) \n \n | \n |
\n Sarah Bö\;hme \n Pfizer | \n Indirect comparis
ons with and without adjustment for patient characteristics within the fra
mework of AMNOG \n \n Abstract: \;Within the framework of the Act on the Reform of the Market for Medi cinal Products (AMNOG) in Germany\, indirect comparisons are allowed to as sess the extent of added benefit in case of a lack of direct evidence. The method proposed by Bucher et al. has been recognized as one of the standa rd approaches to perform adjusted IC. Further alternative methods exist\, e.g. Matching-based approaches\, which aim to overcome different challenge s. However\, all these methods have certain limitations. \n In this talk the statistical properties of the Bucher approach and th
e Matching-adjusted indirect comparison as well as their limitations in pr
actice will be discussed. | \n
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\n \;\n Mul tilevel network meta-regression for population adjustment based on individ ual and aggregate level data \n Abstract: Standard network meta-analysis (NMA) and indirect comparisons comb ine aggregate data (AgD) from multiple studies on treatments of interest\, assuming that any effect modifiers are balanced across populations. We ca n relax this assumption if individual patient data (IPD) are available fro m all studies by fitting an IPD meta-regression. However\, in many cases I PD are only available from a subset of studies.\nIn the sim plest scenario\, IPD are available for an AB study but only AgD for an AC study. Methods such as Matching Adjusted Indirect Comparison (MAIC) create a population-adjusted indirect comparison between treatments B and C. How ever\, the resulting comparison is only valid in the AC population without additional assumptions\, and the methods cannot be extended to larger tre atment networks. Meta-regression-based approaches can be used in larger ne tworks. However\, these typically fit the same model at both the individua l and aggregate level which incurs aggregation bias. \nW e propose a general method for synthesising evidence from individual and a ggregate data in networks of all sizes\, Multilevel Network Meta-Regressio n\, extending the standard NMA framework. An individual-level regression m odel is defined\, and aggregate study data are fitted by integrating this model over the covariate distributions of the respective studies. Since in tegration is often complex or even intractable\, we take a flexible numeri cal approach using Quasi-Monte Carlo integration\, allowing for easy imple mentation regardless of model form or complexity. Correlation structures b etween covariates are accounted for using copulae. \nWe illustrate the method using an example and compare the results to those ob tained using current methods. Where heterogeneity may be explained by imba lance in effect modifiers between studies we achieve similar fit to a rand om effects NMA\, but uncertainty is substantially reduced\, and the model is more interpretable. Crucially for decision making\, comparisons may be provided in any target population with a given covariate distribution. \n\n Biography: David Philli ppo is a statistician at the University of Bristol. His research focuses o n methodology for evidence synthesis\, Bayesian Network Meta-Analysis\, an d indirect comparisons. He is the lead author of a recent Technical Suppor t Document published by the NICE Decision Support Unit on population-adjus ted indirect comparisons\, on which he is also undertaking his PhD. \ n \n Click here to view the slides. | \n \;\n Generaliz ability of clinical trial data into real life settings - Yann Ruffieux (University Bern)\, Alan Brnabic (Lilly) \n < /td>\n \n |
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\;\n Combining RCT efficacy data and real-world evidence to predict drug effectiveness &ndash\; a case study in Rheumatoid Arthritis. \n Abstract: \;< /strong>Decision-makers often need to assess the real-world effectiveness of a new drug before it is on the market. We propose a method to predict d rug effectiveness pre-launch\, and apply it in a case study in rheumatoid arthritis. Our approach comprises several steps: 1) identify an existing t reatment similar to the new drug\, 2) quantify the impact of treatment\, p rognostic factors\, and effect modifiers on clinical outcome\, 3) determin e the characteristics of patients likely to receive the new drug in routin e care\, 4) predict treatment outcome for patients with these characterist ics.\n \n Biography: \;Yann Ruffieux is a Statistician at the Institute of Social and Prevent ive Medecine (ISPM) in Bern\, Switzerland. He has an MSc in Mathematical E ngineering from the Swiss Federal Institute of Technology in Lausanne (EPF L). After briefly working in pharma as Biostatistician\, he joined ISPM in 2015\, where he has contributed to the GetReal project and to HIV-related epidemiological research. \n \n Click here to view the slides. \n | \n
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\;\n Reweighting randomized cont rolled trial (RCT) evidence to better reflect real life &ndash\; a case st udy of the Innovation in Medicine initiative using patients with non-small cell lung cancer (NSCLC) \n Abstract: The objective of the presentation will be to present a case study tha t assesses the generalizability of efficacy (overall survival [OS]) from t he pivotal RCT (JMDB) comparing pemetrexed with gemicitabine to treat non- squamous non-small cell lung cancer using real-world data from a prospecti ve observational study (FRAME) using a reweighting approach. Both inverse propensity scoring and entropy balancing were used to reweight the RCT dat a based on the real-world FRAME data in an attempt to mirror routine clini cal practice in the trial setting.\n \n Biography: \;Mr. Brnabic is currently Principal Resea rch Scientist at Eli Lilly working in Real World evidence (RWE) with a foc us on specialized analysis that supports this area. Prior to this he was t he Asia Pacific Director of the Health Outcomes and Health Economics\, Lif e Sciences for OPTUM. Whilst at Eli Lilly he has been the Health Outcomes and Statistics Asia Pacific statistical sciences group leader and manager. His work has included: designing/reviewing and analyzing concepts and stu dies (Phase IIIb &\; IV observational studies)\, as well as leading and reviewing external methodologies/ guidelines for use within the company a s well as consulting/coordinating strategy for analysis on Reimbursement d ossiers &\; other related Health Outcome activities for countries like Australia\, Canada &\; Korea. He worked as a Consultant Biostatistician for 5 years in Public Health NSW Health Department. Following that he was a Senior Biostatistician at the George Institute which is affiliated with UNSW where he worked on epidemiological studies and RCTs. Before joining Eli Lilly he also took a position at the NSW Department of Corrective Serv ices as Deputy Director of the Research &\; Statistics\, Sydney. \ n \n Mr. Brnabic&rsquo\;s interests are in the design and analysis of observational studies with a focus on methodolo gies related to subgroup identification as well as selection bias adjustme nt tools including matching\, propensity score analysis and local control. He is also interested in Health Outcomes and statistical approaches used to help support the reimbursement of medicines like matched adjusted indir ect comparisons as well as mixed treatment comparisons. \n < p>He has A-STAT Professional Accreditation with the Statistical Society of Australia (SSAI). He is co-chair and previous Chair for the Australian Ph armaceutical Biostatistics Group (APBG).\n \n Click here to vie w the slides.\n | \n
| \;\n Cross-design approach es combining observational and clinical trial data - Mark Belger (Lill y)\, Keith Abrams (University Leicester) \n \n | |
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Cross-design approaches combining observational and clin ical trial data for HTA \n\n \n \n Efthimiou O\, Mavridis D1\, Debray TP\, Samara M\, Belger M\, Siontis GC\, Leucht S\, Salanti G\; GetReal Wo rk Package 4. Combining randomized and non-randomized evidence in network meta-analysis. Stat Med. 2017 Apr 15\;36(8):1210-1226. doi: 10.1002/sim.72 23. Epub 2017 Jan 12 \n Biography: M ark Belger been a statistician for the last 34 years mainly working in the area of non-RCT studies. I joined the pharmaceutical industry 14 years ag o prior to that I worked in the NHS. I draw from extensive experience of c onducting studies in Non RCT populations from both an industry and non-ind ustry perspective. My current responsibilities with Eli Lilly are to suppo rt the companies submissions to HTA&rsquo\;s with a focus on our Neurodege neration and pain indications. In addition\, I also lead on a number of Re al World analytical methodological projects within the company. I was an a ctive member of IMI &ldquo\;GetReal&rdquo\;\, and I am currently involved in two Alzheimer&rsquo\;s disease IMI projects &ldquo\;ROADMAP&rdquo\; and &ldquo\;MOPEAD&rdquo\;. I have co-authored publications that focus on met hods for analysing non-RCT data\, and clinical papers reporting results fr om non-RCT studies conducted by Eli Lilly.\n \n Click here to vi ew the slides. \n \n | \n
 \n \n Keith Abrams\, | \n \;\n
Incorporating Real World Evidence (RWE) in Network M eta-Analysis (NMA) &ndash\; Experiences from the Innovative Medicines Init iative (IMI) GetReal Project \n Abstract: In this talk the possible situations in which Real World Evidence (RWE)\, both comparative and single arm studies\, could be included in a N etwork Meta-Analysis (NMA) will be described and discussed. These include\ ; sparse networks\, disconnected networks\, multiple outcome networks\, an d the use of such NMAs in terms of decision making and designing future Ra ndomised Controlled Trials (RCTs). In particular\, methods for the allowan ce of potential biases and selection effects associated with RWE and how t hese may also be incorporated into NMAs will be discussed. The methods wil l be illustrated using examples from the IMI GetReal Project on patients w ith Multiple Sclerosis or Rheumatoid Arthritis. \;\n \n Biography: \;Keith Abrams is Prof essor of Medical Statistics\, within the Department of Health Sciences at the University of Leicester\, where he heads the Biostatistics Research Gr oup. His research interests\, for which he has an international reputation \, are primarily concerned with the development and application of Bayesia n statistical methods in Health Technology Assessment (HTA)\, in particula r regarding clinical trials\, evidence synthesis\, and economic decision m odelling\, and Non-Communicable Disease (NCD) epidemiology. This work is p rimarily supported with funding from EU\, Medical Research Council (MRC)\, National Institute for Health Research (NIHR) and industry (with a total value in excess of £\;20M over the last 5 years). Prof Abrams has bee n extensively involved with the UK NIHR HTA Programme and UK National Inst itute for Health &\; Care Excellence (NICE) appraisal process since the ir inception. He was a member of the NICE Technology Appraisals Committee for over 8 years until 2015\, is a member of the NICE Decision Support Uni t and NICE Technical Support Unit\, acts as a consultant to the NICE Scien tific Advice Programme\, and is a NIHR Senior Investigator Emeritus. \ ; He is also a Fellow of the Royal Statistical Society\, and a Chartered S tatistician. He has published widely in both substantive and methodologica l areas [h-index 69] including co-authoring books on Methods for Meta-Anal ysis in Medical Research\, Bayesian Approaches to Clinical Trials and Heal thcare Evaluation\, and Evidence Synthesis for Decision Making in Healthca re\, in addition to co-editing one of the first texts on Methods for Evide nce-based Healthcare. Prof Abrams has extensive experience over the last 2 5 years as a consultant to both the pharmaceutical and healthcare consulta ncy sectors\, providing both methodological and strategic HTA advice acros s a wide range of therapeutic areas. \; \; \n \n Click he re to view the slides. \n | \n
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