Stevenage , UK
Use of extrapolation techniques is playing an increasingly important role in the development of new medicines particularly with regard to special populations such as paediatrics and rare diseases. This meeting will include speakers from industry, academia and regulatory (including Rob Hemmings from MHRA).
Speakers include:
Rob Hemmings, MHRA
Extrapolation; regulatory need, examples and emerging guidance.
Abstract: Extrapolation is defined as ‘extending information and conclusions available from studies in one or more subgroups of the patient population (source population(s)), or in related conditions or with related medicinal products, to make inferences for another subgroup of the population (target population), or condition or product, thus reducing the amount of, or general need for, additional information (types of studies, design modifications, number of patients required) needed to reach conclusions for the target population, or condition or medicinal product’. The talk will illustrate the potential need for, and benefits of, this concept in regulatory work with a primary focus on extrapolation from adults to children. An overview of the EMA Reflection Paper on this topic will be presented and discussed, highlighting areas for further discussion and research.
Ian Wadsworth, Lisa V. Hampson, Thomas Jaki and Graeme J. Sills
Using historical data to inform extrapolation decisions in children
When developing a new medicine for children, the potential to extrapolate from adult efficacy data is well recognised. However, significant assumptions about the similarity of adults and children are needed for extrapolations to be biologically plausible. One such assumption is that pharmacokinetic-pharmacodynamic (PK-PD) relationships are similar in these different groups. In this presentation, we consider how ‘source’ data available from historical trials completed in adults and adolescents treated with a test drug, can be used to quantify prior uncertainty about whether PK-PD relationships are similar in adults and younger children. A Bayesian multivariate meta-analytic model is used to synthesise the PK-PD data available from the historical trials which recruited adults and adolescents. The model adjusts for the biases that may arise since these existing data are not perfectly relevant to the comparison of interest, and we propose a strategy for eliciting expert prior opinion on the size of these external biases. From the fitted bias-adjusted meta-analytic model we derive prior distributions which quantify our uncertainty about the similarity of PK-PD relationships in adults and younger children. These prior distributions can then be used to calculate the probability of similar PK-PD relationships in adults and younger children which, in turn, may be used to inform decisions as to whether complete extrapolation of efficacy data from adults to children is currently justified, or whether additional data in children are needed to reduce uncertainty. Properties of the proposed methods are assessed using simulation, and their application to epilepsy drug development is considered.
Clara Domínguez-Islas1, Adrian Mander1, Rebecca Turner2, Nicky Best3
A Bayesian framework for extrapolation using mixture priors
1 MRC Biostatistics Unit, University of Cambridge, UK.
2 MRC Clinical Trials Unit, University College London, UK.
3 GlaxoSmithKline, UK.
As defined by the European Medicines Agency (EMA), extrapolation refers to the extension of information and conclusions available from studies in a source population to make inferences in a target population, in order to reduce the amount of additional information needed to reach conclusions for the latter. Bayesian inference seems to provide a natural framework to implement the extrapolation principle, as the information from the source population can be used as the prior beliefs for the target population. However, intrinsic to extrapolation principle, there is also the belief that the source and target populations, although similar enough to allow one of them to inform the other, are not exactly the same and important differences, not known a priori, might exist. Therefore, along with informative priors, we also need to incorporate a certain degree of scepticism. This could be achieved by the use of mixture priors. Although mixture priors have been already proposed in different extrapolation contexts (bridging studies, historical controls, paediatric extrapolation), we identify some gaps in the research conducted and reported so far. In this presentation, we intend to further explore and better understand the potential of mixture priors to provide a quantitative framework for extrapolation. First we present the mixture prior model with special emphasis on the interpretation and type of inference that it allows, providing a connection with Bayesian model averaging. We then address some of the challenges that arise when constructing a mixture prior, including the choices to be made for each of the components of the model, as well as technical aspects of the estimation and computation. Finally, we discuss the frequentist operating characteristics of this approach and identify the trade-offs that come with the flexibility and robustness of the mixture priors.
Lisa V Hampson, Franz Koenig
New medicines for children should be subject to rigorous examination whilst taking steps to avoid unnecessary experimentation. Extrapolating from adult data can reduce uncertainty about a drug’s effects in younger patients meaning smaller trials may suffice.
We consider how to design a confirmatory trial in children intended to compare the efficacy of a new drug, E, against control. Assuming that conduct of this trial is conditional on having demonstrated a significant beneficial effect in adults, we adopt a Bayesian approach to incorporate these adult data into the design and analysis of the paediatric trial. At each stage, inferences are made using all available data to update a Bayesian mixture model for prior opinion on the degree of similarities between adults and children. Using this framework, we propose designs for the paediatric trial which are specified by calibrating the sample size and final decision rule to: a) achieve a high frequentist power and high minimum (or average) Bayesian positive predictive value of a significant result in children; or b) ensure that a final decision to adopt (abandon) drug E in children is always associated with a minimum positive (negative) predictive value. Operating characteristics of our Bayesian designs are evaluated and compared with those of a recently proposed hybrid approach (Hlavin et al. Statistics in Medicine 2016; 35: 2117) where the sample size and significance level of a frequentist confirmatory trial in children are set to achieve a high frequentist power and high average positive predictive value of a significant result in children.
Nicky Best, Dawn Edwards
A case study using Bayesian methods to leverage existing clinical efficacy data in paediatric trials
Recently there has been increased regulatory interest in partial extrapolation of adult efficacy information to paediatrics populations to reduce data collection requirements in children. In this talk we will present a case study describing plans to use partial extrapolation of adult efficacy data from a phase III trial of an experimental drug in adolescents with a respiratory disease. We will demonstrate how adult data on the treatment difference for the endpoint of interest can be included via an informative prior distribution to increase the probability of success of the study in adolescents and the precision of the estimated treatment difference. A method which incorporates dynamic borrowing will be used to define the level of extrapolation using a 2-step approach whereby information from the adult data is first incorporated into a prior distribution before being integrated with the data from the adolescent population. We propose a 3-component weighted robust mixture prior with the informative components based on (1) the adult efficacy data, (2) rescaled adult efficacy data to reflect the expected response for the adolescent population, and (3) a flat component to ensure that, in the event the adolescent and adult data are in clear conflict, the latter will have minimal influence on the posterior distribution of the treatment difference, thus also preventing excessive inflation of type 1 error. We will present results of a simulation study investigating operating characteristics for different choices of success criteria and prior weights.
Peter Milligan
Utilizing a Quantitative Framework to support Extrapolation
Peter A Milligan on behalf of the EFPIA MID3 Workgroup
The 2016 white paper on Good Practices in Model-Informed Drug Discovery and Development: Practice, Application, and Documentation (1) defines Model Informed Drug Discovery and Development (MID3) as a ‘‘quantitative framework for prediction and extrapolation, centred on knowledge and inference generated from integrated models of compound, mechanism and disease level data and aimed at improving the quality, efficiency and cost effectiveness of decision making’’.
MID3 in its simplest form embodies using ‘‘fit-for-purpose’’ mathematical models, implemented according to good practices, in order to enhance the extraction of inference from both existing information and data emanating from ongoing experiments. As the underpinning foundations for MID3 are based on robust scientific principles derived from pharmacological, physiological, and pathological processes (the domain sciences), MID3 can more effectively support translation across, and extrapolation beyond, the direct inference obtained from standard descriptive methods applied to experimental data.
Conversion of the current knowledge captured within the ‘‘fit-for-purpose’’ model into inference requires a prediction based either on particular model parameter estimates or utilizing values generated through simulation. Predictions can either be interpolative or extrapolative with respect to available evidence and the intended purpose.
Extrapolations beyond current experience often provide the greatest value to pharmaceutical companies. The recent EMA concept paper on The Extrapolation of Efficacy and Safety Data in Medicine Development (2) identified the approaches utilized in MID3 as part of the extrapolation concept and for inclusion in an extrapolation plan. The use of extrapolations emanating from an appropriate quantitative framework to bridge efficacy and safety in special populations was discussed in the 2011 joint workshop (3) with some of the resultant proposals subsequently published in greater detail (4). Replacement of direct experimental evidence (including all or part of a clinical trial) in a development program is conceptually ‘‘permissible’’ but considered to be of ‘‘high regulatory impact’’ (5) necessitating substantive a priori discussions with the regulatory agencies to characterize the context of use for any resultant extrapolations.
Stevenage , UK
Use of extrapolation techniques is playing an increasingly important role in the development of new medicines particularly with regard to special populations such as paediatrics and rare diseases. This meeting will include speakers from industry, academia and regulatory (including Rob Hemmings from MHRA).
Speakers include:
Rob Hemmings, MHRA
Extrapolation; regulatory need, examples and emerging guidance.
Abstract: Extrapolation is defined as ‘extending information and conclusions available from studies in one or more subgroups of the patient population (source population(s)), or in related conditions or with related medicinal products, to make inferences for another subgroup of the population (target population), or condition or product, thus reducing the amount of, or general need for, additional information (types of studies, design modifications, number of patients required) needed to reach conclusions for the target population, or condition or medicinal product’. The talk will illustrate the potential need for, and benefits of, this concept in regulatory work with a primary focus on extrapolation from adults to children. An overview of the EMA Reflection Paper on this topic will be presented and discussed, highlighting areas for further discussion and research.
Ian Wadsworth, Lisa V. Hampson, Thomas Jaki and Graeme J. Sills
Using historical data to inform extrapolation decisions in children
When developing a new medicine for children, the potential to extrapolate from adult efficacy data is well recognised. However, significant assumptions about the similarity of adults and children are needed for extrapolations to be biologically plausible. One such assumption is that pharmacokinetic-pharmacodynamic (PK-PD) relationships are similar in these different groups. In this presentation, we consider how ‘source’ data available from historical trials completed in adults and adolescents treated with a test drug, can be used to quantify prior uncertainty about whether PK-PD relationships are similar in adults and younger children. A Bayesian multivariate meta-analytic model is used to synthesise the PK-PD data available from the historical trials which recruited adults and adolescents. The model adjusts for the biases that may arise since these existing data are not perfectly relevant to the comparison of interest, and we propose a strategy for eliciting expert prior opinion on the size of these external biases. From the fitted bias-adjusted meta-analytic model we derive prior distributions which quantify our uncertainty about the similarity of PK-PD relationships in adults and younger children. These prior distributions can then be used to calculate the probability of similar PK-PD relationships in adults and younger children which, in turn, may be used to inform decisions as to whether complete extrapolation of efficacy data from adults to children is currently justified, or whether additional data in children are needed to reduce uncertainty. Properties of the proposed methods are assessed using simulation, and their application to epilepsy drug development is considered.
Clara Domínguez-Islas1, Adrian Mander1, Rebecca Turner2, Nicky Best3
A Bayesian framework for extrapolation using mixture priors
1 MRC Biostatistics Unit, University of Cambridge, UK.
2 MRC Clinical Trials Unit, University College London, UK.
3 GlaxoSmithKline, UK.
As defined by the European Medicines Agency (EMA), extrapolation refers to the extension of information and conclusions available from studies in a source population to make inferences in a target population, in order to reduce the amount of additional information needed to reach conclusions for the latter. Bayesian inference seems to provide a natural framework to implement the extrapolation principle, as the information from the source population can be used as the prior beliefs for the target population. However, intrinsic to extrapolation principle, there is also the belief that the source and target populations, although similar enough to allow one of them to inform the other, are not exactly the same and important differences, not known a priori, might exist. Therefore, along with informative priors, we also need to incorporate a certain degree of scepticism. This could be achieved by the use of mixture priors. Although mixture priors have been already proposed in different extrapolation contexts (bridging studies, historical controls, paediatric extrapolation), we identify some gaps in the research conducted and reported so far. In this presentation, we intend to further explore and better understand the potential of mixture priors to provide a quantitative framework for extrapolation. First we present the mixture prior model with special emphasis on the interpretation and type of inference that it allows, providing a connection with Bayesian model averaging. We then address some of the challenges that arise when constructing a mixture prior, including the choices to be made for each of the components of the model, as well as technical aspects of the estimation and computation. Finally, we discuss the frequentist operating characteristics of this approach and identify the trade-offs that come with the flexibility and robustness of the mixture priors.
Lisa V Hampson, Franz Koenig
New medicines for children should be subject to rigorous examination whilst taking steps to avoid unnecessary experimentation. Extrapolating from adult data can reduce uncertainty about a drug’s effects in younger patients meaning smaller trials may suffice.
We consider how to design a confirmatory trial in children intended to compare the efficacy of a new drug, E, against control. Assuming that conduct of this trial is conditional on having demonstrated a significant beneficial effect in adults, we adopt a Bayesian approach to incorporate these adult data into the design and analysis of the paediatric trial. At each stage, inferences are made using all available data to update a Bayesian mixture model for prior opinion on the degree of similarities between adults and children. Using this framework, we propose designs for the paediatric trial which are specified by calibrating the sample size and final decision rule to: a) achieve a high frequentist power and high minimum (or average) Bayesian positive predictive value of a significant result in children; or b) ensure that a final decision to adopt (abandon) drug E in children is always associated with a minimum positive (negative) predictive value. Operating characteristics of our Bayesian designs are evaluated and compared with those of a recently proposed hybrid approach (Hlavin et al. Statistics in Medicine 2016; 35: 2117) where the sample size and significance level of a frequentist confirmatory trial in children are set to achieve a high frequentist power and high average positive predictive value of a significant result in children.
Nicky Best, Dawn Edwards
A case study using Bayesian methods to leverage existing clinical efficacy data in paediatric trials
Recently there has been increased regulatory interest in partial extrapolation of adult efficacy information to paediatrics populations to reduce data collection requirements in children. In this talk we will present a case study describing plans to use partial extrapolation of adult efficacy data from a phase III trial of an experimental drug in adolescents with a respiratory disease. We will demonstrate how adult data on the treatment difference for the endpoint of interest can be included via an informative prior distribution to increase the probability of success of the study in adolescents and the precision of the estimated treatment difference. A method which incorporates dynamic borrowing will be used to define the level of extrapolation using a 2-step approach whereby information from the adult data is first incorporated into a prior distribution before being integrated with the data from the adolescent population. We propose a 3-component weighted robust mixture prior with the informative components based on (1) the adult efficacy data, (2) rescaled adult efficacy data to reflect the expected response for the adolescent population, and (3) a flat component to ensure that, in the event the adolescent and adult data are in clear conflict, the latter will have minimal influence on the posterior distribution of the treatment difference, thus also preventing excessive inflation of type 1 error. We will present results of a simulation study investigating operating characteristics for different choices of success criteria and prior weights.
Peter Milligan
Utilizing a Quantitative Framework to support Extrapolation
Peter A Milligan on behalf of the EFPIA MID3 Workgroup
The 2016 white paper on Good Practices in Model-Informed Drug Discovery and Development: Practice, Application, and Documentation (1) defines Model Informed Drug Discovery and Development (MID3) as a ‘‘quantitative framework for prediction and extrapolation, centred on knowledge and inference generated from integrated models of compound, mechanism and disease level data and aimed at improving the quality, efficiency and cost effectiveness of decision making’’.
MID3 in its simplest form embodies using ‘‘fit-for-purpose’’ mathematical models, implemented according to good practices, in order to enhance the extraction of inference from both existing information and data emanating from ongoing experiments. As the underpinning foundations for MID3 are based on robust scientific principles derived from pharmacological, physiological, and pathological processes (the domain sciences), MID3 can more effectively support translation across, and extrapolation beyond, the direct inference obtained from standard descriptive methods applied to experimental data.
Conversion of the current knowledge captured within the ‘‘fit-for-purpose’’ model into inference requires a prediction based either on particular model parameter estimates or utilizing values generated through simulation. Predictions can either be interpolative or extrapolative with respect to available evidence and the intended purpose.
Extrapolations beyond current experience often provide the greatest value to pharmaceutical companies. The recent EMA concept paper on The Extrapolation of Efficacy and Safety Data in Medicine Development (2) identified the approaches utilized in MID3 as part of the extrapolation concept and for inclusion in an extrapolation plan. The use of extrapolations emanating from an appropriate quantitative framework to bridge efficacy and safety in special populations was discussed in the 2011 joint workshop (3) with some of the resultant proposals subsequently published in greater detail (4). Replacement of direct experimental evidence (including all or part of a clinical trial) in a development program is conceptually ‘‘permissible’’ but considered to be of ‘‘high regulatory impact’’ (5) necessitating substantive a priori discussions with the regulatory agencies to characterize the context of use for any resultant extrapolations.
Stevenage , UK
Use of extrapolation techniques is playing an increasingly important role in the development of new medicines particularly with regard to special populations such as paediatrics and rare diseases. This meeting will include speakers from industry, academia and regulatory (including Rob Hemmings from MHRA).
Speakers include:
Rob Hemmings, MHRA
Extrapolation; regulatory need, examples and emerging guidance.
Abstract: Extrapolation is defined as ‘extending information and conclusions available from studies in one or more subgroups of the patient population (source population(s)), or in related conditions or with related medicinal products, to make inferences for another subgroup of the population (target population), or condition or product, thus reducing the amount of, or general need for, additional information (types of studies, design modifications, number of patients required) needed to reach conclusions for the target population, or condition or medicinal product’. The talk will illustrate the potential need for, and benefits of, this concept in regulatory work with a primary focus on extrapolation from adults to children. An overview of the EMA Reflection Paper on this topic will be presented and discussed, highlighting areas for further discussion and research.
Ian Wadsworth, Lisa V. Hampson, Thomas Jaki and Graeme J. Sills
Using historical data to inform extrapolation decisions in children
When developing a new medicine for children, the potential to extrapolate from adult efficacy data is well recognised. However, significant assumptions about the similarity of adults and children are needed for extrapolations to be biologically plausible. One such assumption is that pharmacokinetic-pharmacodynamic (PK-PD) relationships are similar in these different groups. In this presentation, we consider how ‘source’ data available from historical trials completed in adults and adolescents treated with a test drug, can be used to quantify prior uncertainty about whether PK-PD relationships are similar in adults and younger children. A Bayesian multivariate meta-analytic model is used to synthesise the PK-PD data available from the historical trials which recruited adults and adolescents. The model adjusts for the biases that may arise since these existing data are not perfectly relevant to the comparison of interest, and we propose a strategy for eliciting expert prior opinion on the size of these external biases. From the fitted bias-adjusted meta-analytic model we derive prior distributions which quantify our uncertainty about the similarity of PK-PD relationships in adults and younger children. These prior distributions can then be used to calculate the probability of similar PK-PD relationships in adults and younger children which, in turn, may be used to inform decisions as to whether complete extrapolation of efficacy data from adults to children is currently justified, or whether additional data in children are needed to reduce uncertainty. Properties of the proposed methods are assessed using simulation, and their application to epilepsy drug development is considered.
Clara Domínguez-Islas1, Adrian Mander1, Rebecca Turner2, Nicky Best3
A Bayesian framework for extrapolation using mixture priors
1 MRC Biostatistics Unit, University of Cambridge, UK.
2 MRC Clinical Trials Unit, University College London, UK.
3 GlaxoSmithKline, UK.
As defined by the European Medicines Agency (EMA), extrapolation refers to the extension of information and conclusions available from studies in a source population to make inferences in a target population, in order to reduce the amount of additional information needed to reach conclusions for the latter. Bayesian inference seems to provide a natural framework to implement the extrapolation principle, as the information from the source population can be used as the prior beliefs for the target population. However, intrinsic to extrapolation principle, there is also the belief that the source and target populations, although similar enough to allow one of them to inform the other, are not exactly the same and important differences, not known a priori, might exist. Therefore, along with informative priors, we also need to incorporate a certain degree of scepticism. This could be achieved by the use of mixture priors. Although mixture priors have been already proposed in different extrapolation contexts (bridging studies, historical controls, paediatric extrapolation), we identify some gaps in the research conducted and reported so far. In this presentation, we intend to further explore and better understand the potential of mixture priors to provide a quantitative framework for extrapolation. First we present the mixture prior model with special emphasis on the interpretation and type of inference that it allows, providing a connection with Bayesian model averaging. We then address some of the challenges that arise when constructing a mixture prior, including the choices to be made for each of the components of the model, as well as technical aspects of the estimation and computation. Finally, we discuss the frequentist operating characteristics of this approach and identify the trade-offs that come with the flexibility and robustness of the mixture priors.
Lisa V Hampson, Franz Koenig
New medicines for children should be subject to rigorous examination whilst taking steps to avoid unnecessary experimentation. Extrapolating from adult data can reduce uncertainty about a drug’s effects in younger patients meaning smaller trials may suffice.
We consider how to design a confirmatory trial in children intended to compare the efficacy of a new drug, E, against control. Assuming that conduct of this trial is conditional on having demonstrated a significant beneficial effect in adults, we adopt a Bayesian approach to incorporate these adult data into the design and analysis of the paediatric trial. At each stage, inferences are made using all available data to update a Bayesian mixture model for prior opinion on the degree of similarities between adults and children. Using this framework, we propose designs for the paediatric trial which are specified by calibrating the sample size and final decision rule to: a) achieve a high frequentist power and high minimum (or average) Bayesian positive predictive value of a significant result in children; or b) ensure that a final decision to adopt (abandon) drug E in children is always associated with a minimum positive (negative) predictive value. Operating characteristics of our Bayesian designs are evaluated and compared with those of a recently proposed hybrid approach (Hlavin et al. Statistics in Medicine 2016; 35: 2117) where the sample size and significance level of a frequentist confirmatory trial in children are set to achieve a high frequentist power and high average positive predictive value of a significant result in children.
Nicky Best, Dawn Edwards
A case study using Bayesian methods to leverage existing clinical efficacy data in paediatric trials
Recently there has been increased regulatory interest in partial extrapolation of adult efficacy information to paediatrics populations to reduce data collection requirements in children. In this talk we will present a case study describing plans to use partial extrapolation of adult efficacy data from a phase III trial of an experimental drug in adolescents with a respiratory disease. We will demonstrate how adult data on the treatment difference for the endpoint of interest can be included via an informative prior distribution to increase the probability of success of the study in adolescents and the precision of the estimated treatment difference. A method which incorporates dynamic borrowing will be used to define the level of extrapolation using a 2-step approach whereby information from the adult data is first incorporated into a prior distribution before being integrated with the data from the adolescent population. We propose a 3-component weighted robust mixture prior with the informative components based on (1) the adult efficacy data, (2) rescaled adult efficacy data to reflect the expected response for the adolescent population, and (3) a flat component to ensure that, in the event the adolescent and adult data are in clear conflict, the latter will have minimal influence on the posterior distribution of the treatment difference, thus also preventing excessive inflation of type 1 error. We will present results of a simulation study investigating operating characteristics for different choices of success criteria and prior weights.
Peter Milligan
Utilizing a Quantitative Framework to support Extrapolation
Peter A Milligan on behalf of the EFPIA MID3 Workgroup
The 2016 white paper on Good Practices in Model-Informed Drug Discovery and Development: Practice, Application, and Documentation (1) defines Model Informed Drug Discovery and Development (MID3) as a ‘‘quantitative framework for prediction and extrapolation, centred on knowledge and inference generated from integrated models of compound, mechanism and disease level data and aimed at improving the quality, efficiency and cost effectiveness of decision making’’.
MID3 in its simplest form embodies using ‘‘fit-for-purpose’’ mathematical models, implemented according to good practices, in order to enhance the extraction of inference from both existing information and data emanating from ongoing experiments. As the underpinning foundations for MID3 are based on robust scientific principles derived from pharmacological, physiological, and pathological processes (the domain sciences), MID3 can more effectively support translation across, and extrapolation beyond, the direct inference obtained from standard descriptive methods applied to experimental data.
Conversion of the current knowledge captured within the ‘‘fit-for-purpose’’ model into inference requires a prediction based either on particular model parameter estimates or utilizing values generated through simulation. Predictions can either be interpolative or extrapolative with respect to available evidence and the intended purpose.
Extrapolations beyond current experience often provide the greatest value to pharmaceutical companies. The recent EMA concept paper on The Extrapolation of Efficacy and Safety Data in Medicine Development (2) identified the approaches utilized in MID3 as part of the extrapolation concept and for inclusion in an extrapolation plan. The use of extrapolations emanating from an appropriate quantitative framework to bridge efficacy and safety in special populations was discussed in the 2011 joint workshop (3) with some of the resultant proposals subsequently published in greater detail (4). Replacement of direct experimental evidence (including all or part of a clinical trial) in a development program is conceptually ‘‘permissible’’ but considered to be of ‘‘high regulatory impact’’ (5) necessitating substantive a priori discussions with the regulatory agencies to characterize the context of use for any resultant extrapolations.
Stevenage , UK
Use of extrapolation techniques is playing an increasingly important role in the development of new medicines particularly with regard to special populations such as paediatrics and rare diseases. This meeting will include speakers from industry, academia and regulatory (including Rob Hemmings from MHRA).
Speakers include:
Rob Hemmings, MHRA
Extrapolation; regulatory need, examples and emerging guidance.
Abstract: Extrapolation is defined as ‘extending information and conclusions available from studies in one or more subgroups of the patient population (source population(s)), or in related conditions or with related medicinal products, to make inferences for another subgroup of the population (target population), or condition or product, thus reducing the amount of, or general need for, additional information (types of studies, design modifications, number of patients required) needed to reach conclusions for the target population, or condition or medicinal product’. The talk will illustrate the potential need for, and benefits of, this concept in regulatory work with a primary focus on extrapolation from adults to children. An overview of the EMA Reflection Paper on this topic will be presented and discussed, highlighting areas for further discussion and research.
Ian Wadsworth, Lisa V. Hampson, Thomas Jaki and Graeme J. Sills
Using historical data to inform extrapolation decisions in children
When developing a new medicine for children, the potential to extrapolate from adult efficacy data is well recognised. However, significant assumptions about the similarity of adults and children are needed for extrapolations to be biologically plausible. One such assumption is that pharmacokinetic-pharmacodynamic (PK-PD) relationships are similar in these different groups. In this presentation, we consider how ‘source’ data available from historical trials completed in adults and adolescents treated with a test drug, can be used to quantify prior uncertainty about whether PK-PD relationships are similar in adults and younger children. A Bayesian multivariate meta-analytic model is used to synthesise the PK-PD data available from the historical trials which recruited adults and adolescents. The model adjusts for the biases that may arise since these existing data are not perfectly relevant to the comparison of interest, and we propose a strategy for eliciting expert prior opinion on the size of these external biases. From the fitted bias-adjusted meta-analytic model we derive prior distributions which quantify our uncertainty about the similarity of PK-PD relationships in adults and younger children. These prior distributions can then be used to calculate the probability of similar PK-PD relationships in adults and younger children which, in turn, may be used to inform decisions as to whether complete extrapolation of efficacy data from adults to children is currently justified, or whether additional data in children are needed to reduce uncertainty. Properties of the proposed methods are assessed using simulation, and their application to epilepsy drug development is considered.
Clara Domínguez-Islas1, Adrian Mander1, Rebecca Turner2, Nicky Best3
A Bayesian framework for extrapolation using mixture priors
1 MRC Biostatistics Unit, University of Cambridge, UK.
2 MRC Clinical Trials Unit, University College London, UK.
3 GlaxoSmithKline, UK.
As defined by the European Medicines Agency (EMA), extrapolation refers to the extension of information and conclusions available from studies in a source population to make inferences in a target population, in order to reduce the amount of additional information needed to reach conclusions for the latter. Bayesian inference seems to provide a natural framework to implement the extrapolation principle, as the information from the source population can be used as the prior beliefs for the target population. However, intrinsic to extrapolation principle, there is also the belief that the source and target populations, although similar enough to allow one of them to inform the other, are not exactly the same and important differences, not known a priori, might exist. Therefore, along with informative priors, we also need to incorporate a certain degree of scepticism. This could be achieved by the use of mixture priors. Although mixture priors have been already proposed in different extrapolation contexts (bridging studies, historical controls, paediatric extrapolation), we identify some gaps in the research conducted and reported so far. In this presentation, we intend to further explore and better understand the potential of mixture priors to provide a quantitative framework for extrapolation. First we present the mixture prior model with special emphasis on the interpretation and type of inference that it allows, providing a connection with Bayesian model averaging. We then address some of the challenges that arise when constructing a mixture prior, including the choices to be made for each of the components of the model, as well as technical aspects of the estimation and computation. Finally, we discuss the frequentist operating characteristics of this approach and identify the trade-offs that come with the flexibility and robustness of the mixture priors.
Lisa V Hampson, Franz Koenig
New medicines for children should be subject to rigorous examination whilst taking steps to avoid unnecessary experimentation. Extrapolating from adult data can reduce uncertainty about a drug’s effects in younger patients meaning smaller trials may suffice.
We consider how to design a confirmatory trial in children intended to compare the efficacy of a new drug, E, against control. Assuming that conduct of this trial is conditional on having demonstrated a significant beneficial effect in adults, we adopt a Bayesian approach to incorporate these adult data into the design and analysis of the paediatric trial. At each stage, inferences are made using all available data to update a Bayesian mixture model for prior opinion on the degree of similarities between adults and children. Using this framework, we propose designs for the paediatric trial which are specified by calibrating the sample size and final decision rule to: a) achieve a high frequentist power and high minimum (or average) Bayesian positive predictive value of a significant result in children; or b) ensure that a final decision to adopt (abandon) drug E in children is always associated with a minimum positive (negative) predictive value. Operating characteristics of our Bayesian designs are evaluated and compared with those of a recently proposed hybrid approach (Hlavin et al. Statistics in Medicine 2016; 35: 2117) where the sample size and significance level of a frequentist confirmatory trial in children are set to achieve a high frequentist power and high average positive predictive value of a significant result in children.
Nicky Best, Dawn Edwards
A case study using Bayesian methods to leverage existing clinical efficacy data in paediatric trials
Recently there has been increased regulatory interest in partial extrapolation of adult efficacy information to paediatrics populations to reduce data collection requirements in children. In this talk we will present a case study describing plans to use partial extrapolation of adult efficacy data from a phase III trial of an experimental drug in adolescents with a respiratory disease. We will demonstrate how adult data on the treatment difference for the endpoint of interest can be included via an informative prior distribution to increase the probability of success of the study in adolescents and the precision of the estimated treatment difference. A method which incorporates dynamic borrowing will be used to define the level of extrapolation using a 2-step approach whereby information from the adult data is first incorporated into a prior distribution before being integrated with the data from the adolescent population. We propose a 3-component weighted robust mixture prior with the informative components based on (1) the adult efficacy data, (2) rescaled adult efficacy data to reflect the expected response for the adolescent population, and (3) a flat component to ensure that, in the event the adolescent and adult data are in clear conflict, the latter will have minimal influence on the posterior distribution of the treatment difference, thus also preventing excessive inflation of type 1 error. We will present results of a simulation study investigating operating characteristics for different choices of success criteria and prior weights.
Peter Milligan
Utilizing a Quantitative Framework to support Extrapolation
Peter A Milligan on behalf of the EFPIA MID3 Workgroup
The 2016 white paper on Good Practices in Model-Informed Drug Discovery and Development: Practice, Application, and Documentation (1) defines Model Informed Drug Discovery and Development (MID3) as a ‘‘quantitative framework for prediction and extrapolation, centred on knowledge and inference generated from integrated models of compound, mechanism and disease level data and aimed at improving the quality, efficiency and cost effectiveness of decision making’’.
MID3 in its simplest form embodies using ‘‘fit-for-purpose’’ mathematical models, implemented according to good practices, in order to enhance the extraction of inference from both existing information and data emanating from ongoing experiments. As the underpinning foundations for MID3 are based on robust scientific principles derived from pharmacological, physiological, and pathological processes (the domain sciences), MID3 can more effectively support translation across, and extrapolation beyond, the direct inference obtained from standard descriptive methods applied to experimental data.
Conversion of the current knowledge captured within the ‘‘fit-for-purpose’’ model into inference requires a prediction based either on particular model parameter estimates or utilizing values generated through simulation. Predictions can either be interpolative or extrapolative with respect to available evidence and the intended purpose.
Extrapolations beyond current experience often provide the greatest value to pharmaceutical companies. The recent EMA concept paper on The Extrapolation of Efficacy and Safety Data in Medicine Development (2) identified the approaches utilized in MID3 as part of the extrapolation concept and for inclusion in an extrapolation plan. The use of extrapolations emanating from an appropriate quantitative framework to bridge efficacy and safety in special populations was discussed in the 2011 joint workshop (3) with some of the resultant proposals subsequently published in greater detail (4). Replacement of direct experimental evidence (including all or part of a clinical trial) in a development program is conceptually ‘‘permissible’’ but considered to be of ‘‘high regulatory impact’’ (5) necessitating substantive a priori discussions with the regulatory agencies to characterize the context of use for any resultant extrapolations.
Stevenage , UK
Use of extrapolation techniques is playing an increasingly important role in the development of new medicines particularly with regard to special populations such as paediatrics and rare diseases. This meeting will include speakers from industry, academia and regulatory (including Rob Hemmings from MHRA).
Speakers include:
Rob Hemmings, MHRA
Extrapolation; regulatory need, examples and emerging guidance.
Abstract: Extrapolation is defined as ‘extending information and conclusions available from studies in one or more subgroups of the patient population (source population(s)), or in related conditions or with related medicinal products, to make inferences for another subgroup of the population (target population), or condition or product, thus reducing the amount of, or general need for, additional information (types of studies, design modifications, number of patients required) needed to reach conclusions for the target population, or condition or medicinal product’. The talk will illustrate the potential need for, and benefits of, this concept in regulatory work with a primary focus on extrapolation from adults to children. An overview of the EMA Reflection Paper on this topic will be presented and discussed, highlighting areas for further discussion and research.
Ian Wadsworth, Lisa V. Hampson, Thomas Jaki and Graeme J. Sills
Using historical data to inform extrapolation decisions in children
When developing a new medicine for children, the potential to extrapolate from adult efficacy data is well recognised. However, significant assumptions about the similarity of adults and children are needed for extrapolations to be biologically plausible. One such assumption is that pharmacokinetic-pharmacodynamic (PK-PD) relationships are similar in these different groups. In this presentation, we consider how ‘source’ data available from historical trials completed in adults and adolescents treated with a test drug, can be used to quantify prior uncertainty about whether PK-PD relationships are similar in adults and younger children. A Bayesian multivariate meta-analytic model is used to synthesise the PK-PD data available from the historical trials which recruited adults and adolescents. The model adjusts for the biases that may arise since these existing data are not perfectly relevant to the comparison of interest, and we propose a strategy for eliciting expert prior opinion on the size of these external biases. From the fitted bias-adjusted meta-analytic model we derive prior distributions which quantify our uncertainty about the similarity of PK-PD relationships in adults and younger children. These prior distributions can then be used to calculate the probability of similar PK-PD relationships in adults and younger children which, in turn, may be used to inform decisions as to whether complete extrapolation of efficacy data from adults to children is currently justified, or whether additional data in children are needed to reduce uncertainty. Properties of the proposed methods are assessed using simulation, and their application to epilepsy drug development is considered.
Clara Domínguez-Islas1, Adrian Mander1, Rebecca Turner2, Nicky Best3
A Bayesian framework for extrapolation using mixture priors
1 MRC Biostatistics Unit, University of Cambridge, UK.
2 MRC Clinical Trials Unit, University College London, UK.
3 GlaxoSmithKline, UK.
As defined by the European Medicines Agency (EMA), extrapolation refers to the extension of information and conclusions available from studies in a source population to make inferences in a target population, in order to reduce the amount of additional information needed to reach conclusions for the latter. Bayesian inference seems to provide a natural framework to implement the extrapolation principle, as the information from the source population can be used as the prior beliefs for the target population. However, intrinsic to extrapolation principle, there is also the belief that the source and target populations, although similar enough to allow one of them to inform the other, are not exactly the same and important differences, not known a priori, might exist. Therefore, along with informative priors, we also need to incorporate a certain degree of scepticism. This could be achieved by the use of mixture priors. Although mixture priors have been already proposed in different extrapolation contexts (bridging studies, historical controls, paediatric extrapolation), we identify some gaps in the research conducted and reported so far. In this presentation, we intend to further explore and better understand the potential of mixture priors to provide a quantitative framework for extrapolation. First we present the mixture prior model with special emphasis on the interpretation and type of inference that it allows, providing a connection with Bayesian model averaging. We then address some of the challenges that arise when constructing a mixture prior, including the choices to be made for each of the components of the model, as well as technical aspects of the estimation and computation. Finally, we discuss the frequentist operating characteristics of this approach and identify the trade-offs that come with the flexibility and robustness of the mixture priors.
Lisa V Hampson, Franz Koenig
New medicines for children should be subject to rigorous examination whilst taking steps to avoid unnecessary experimentation. Extrapolating from adult data can reduce uncertainty about a drug’s effects in younger patients meaning smaller trials may suffice.
We consider how to design a confirmatory trial in children intended to compare the efficacy of a new drug, E, against control. Assuming that conduct of this trial is conditional on having demonstrated a significant beneficial effect in adults, we adopt a Bayesian approach to incorporate these adult data into the design and analysis of the paediatric trial. At each stage, inferences are made using all available data to update a Bayesian mixture model for prior opinion on the degree of similarities between adults and children. Using this framework, we propose designs for the paediatric trial which are specified by calibrating the sample size and final decision rule to: a) achieve a high frequentist power and high minimum (or average) Bayesian positive predictive value of a significant result in children; or b) ensure that a final decision to adopt (abandon) drug E in children is always associated with a minimum positive (negative) predictive value. Operating characteristics of our Bayesian designs are evaluated and compared with those of a recently proposed hybrid approach (Hlavin et al. Statistics in Medicine 2016; 35: 2117) where the sample size and significance level of a frequentist confirmatory trial in children are set to achieve a high frequentist power and high average positive predictive value of a significant result in children.
Nicky Best, Dawn Edwards
A case study using Bayesian methods to leverage existing clinical efficacy data in paediatric trials
Recently there has been increased regulatory interest in partial extrapolation of adult efficacy information to paediatrics populations to reduce data collection requirements in children. In this talk we will present a case study describing plans to use partial extrapolation of adult efficacy data from a phase III trial of an experimental drug in adolescents with a respiratory disease. We will demonstrate how adult data on the treatment difference for the endpoint of interest can be included via an informative prior distribution to increase the probability of success of the study in adolescents and the precision of the estimated treatment difference. A method which incorporates dynamic borrowing will be used to define the level of extrapolation using a 2-step approach whereby information from the adult data is first incorporated into a prior distribution before being integrated with the data from the adolescent population. We propose a 3-component weighted robust mixture prior with the informative components based on (1) the adult efficacy data, (2) rescaled adult efficacy data to reflect the expected response for the adolescent population, and (3) a flat component to ensure that, in the event the adolescent and adult data are in clear conflict, the latter will have minimal influence on the posterior distribution of the treatment difference, thus also preventing excessive inflation of type 1 error. We will present results of a simulation study investigating operating characteristics for different choices of success criteria and prior weights.
Peter Milligan
Utilizing a Quantitative Framework to support Extrapolation
Peter A Milligan on behalf of the EFPIA MID3 Workgroup
The 2016 white paper on Good Practices in Model-Informed Drug Discovery and Development: Practice, Application, and Documentation (1) defines Model Informed Drug Discovery and Development (MID3) as a ‘‘quantitative framework for prediction and extrapolation, centred on knowledge and inference generated from integrated models of compound, mechanism and disease level data and aimed at improving the quality, efficiency and cost effectiveness of decision making’’.
MID3 in its simplest form embodies using ‘‘fit-for-purpose’’ mathematical models, implemented according to good practices, in order to enhance the extraction of inference from both existing information and data emanating from ongoing experiments. As the underpinning foundations for MID3 are based on robust scientific principles derived from pharmacological, physiological, and pathological processes (the domain sciences), MID3 can more effectively support translation across, and extrapolation beyond, the direct inference obtained from standard descriptive methods applied to experimental data.
Conversion of the current knowledge captured within the ‘‘fit-for-purpose’’ model into inference requires a prediction based either on particular model parameter estimates or utilizing values generated through simulation. Predictions can either be interpolative or extrapolative with respect to available evidence and the intended purpose.
Extrapolations beyond current experience often provide the greatest value to pharmaceutical companies. The recent EMA concept paper on The Extrapolation of Efficacy and Safety Data in Medicine Development (2) identified the approaches utilized in MID3 as part of the extrapolation concept and for inclusion in an extrapolation plan. The use of extrapolations emanating from an appropriate quantitative framework to bridge efficacy and safety in special populations was discussed in the 2011 joint workshop (3) with some of the resultant proposals subsequently published in greater detail (4). Replacement of direct experimental evidence (including all or part of a clinical trial) in a development program is conceptually ‘‘permissible’’ but considered to be of ‘‘high regulatory impact’’ (5) necessitating substantive a priori discussions with the regulatory agencies to characterize the context of use for any resultant extrapolations.
Stevenage , UK
Use of extrapolation techniques is playing an increasingly important role in the development of new medicines particularly with regard to special populations such as paediatrics and rare diseases. This meeting will include speakers from industry, academia and regulatory (including Rob Hemmings from MHRA).
Speakers include:
Rob Hemmings, MHRA
Extrapolation; regulatory need, examples and emerging guidance.
Abstract: Extrapolation is defined as ‘extending information and conclusions available from studies in one or more subgroups of the patient population (source population(s)), or in related conditions or with related medicinal products, to make inferences for another subgroup of the population (target population), or condition or product, thus reducing the amount of, or general need for, additional information (types of studies, design modifications, number of patients required) needed to reach conclusions for the target population, or condition or medicinal product’. The talk will illustrate the potential need for, and benefits of, this concept in regulatory work with a primary focus on extrapolation from adults to children. An overview of the EMA Reflection Paper on this topic will be presented and discussed, highlighting areas for further discussion and research.
Ian Wadsworth, Lisa V. Hampson, Thomas Jaki and Graeme J. Sills
Using historical data to inform extrapolation decisions in children
When developing a new medicine for children, the potential to extrapolate from adult efficacy data is well recognised. However, significant assumptions about the similarity of adults and children are needed for extrapolations to be biologically plausible. One such assumption is that pharmacokinetic-pharmacodynamic (PK-PD) relationships are similar in these different groups. In this presentation, we consider how ‘source’ data available from historical trials completed in adults and adolescents treated with a test drug, can be used to quantify prior uncertainty about whether PK-PD relationships are similar in adults and younger children. A Bayesian multivariate meta-analytic model is used to synthesise the PK-PD data available from the historical trials which recruited adults and adolescents. The model adjusts for the biases that may arise since these existing data are not perfectly relevant to the comparison of interest, and we propose a strategy for eliciting expert prior opinion on the size of these external biases. From the fitted bias-adjusted meta-analytic model we derive prior distributions which quantify our uncertainty about the similarity of PK-PD relationships in adults and younger children. These prior distributions can then be used to calculate the probability of similar PK-PD relationships in adults and younger children which, in turn, may be used to inform decisions as to whether complete extrapolation of efficacy data from adults to children is currently justified, or whether additional data in children are needed to reduce uncertainty. Properties of the proposed methods are assessed using simulation, and their application to epilepsy drug development is considered.
Clara Domínguez-Islas1, Adrian Mander1, Rebecca Turner2, Nicky Best3
A Bayesian framework for extrapolation using mixture priors
1 MRC Biostatistics Unit, University of Cambridge, UK.
2 MRC Clinical Trials Unit, University College London, UK.
3 GlaxoSmithKline, UK.
As defined by the European Medicines Agency (EMA), extrapolation refers to the extension of information and conclusions available from studies in a source population to make inferences in a target population, in order to reduce the amount of additional information needed to reach conclusions for the latter. Bayesian inference seems to provide a natural framework to implement the extrapolation principle, as the information from the source population can be used as the prior beliefs for the target population. However, intrinsic to extrapolation principle, there is also the belief that the source and target populations, although similar enough to allow one of them to inform the other, are not exactly the same and important differences, not known a priori, might exist. Therefore, along with informative priors, we also need to incorporate a certain degree of scepticism. This could be achieved by the use of mixture priors. Although mixture priors have been already proposed in different extrapolation contexts (bridging studies, historical controls, paediatric extrapolation), we identify some gaps in the research conducted and reported so far. In this presentation, we intend to further explore and better understand the potential of mixture priors to provide a quantitative framework for extrapolation. First we present the mixture prior model with special emphasis on the interpretation and type of inference that it allows, providing a connection with Bayesian model averaging. We then address some of the challenges that arise when constructing a mixture prior, including the choices to be made for each of the components of the model, as well as technical aspects of the estimation and computation. Finally, we discuss the frequentist operating characteristics of this approach and identify the trade-offs that come with the flexibility and robustness of the mixture priors.
Lisa V Hampson, Franz Koenig
New medicines for children should be subject to rigorous examination whilst taking steps to avoid unnecessary experimentation. Extrapolating from adult data can reduce uncertainty about a drug’s effects in younger patients meaning smaller trials may suffice.
We consider how to design a confirmatory trial in children intended to compare the efficacy of a new drug, E, against control. Assuming that conduct of this trial is conditional on having demonstrated a significant beneficial effect in adults, we adopt a Bayesian approach to incorporate these adult data into the design and analysis of the paediatric trial. At each stage, inferences are made using all available data to update a Bayesian mixture model for prior opinion on the degree of similarities between adults and children. Using this framework, we propose designs for the paediatric trial which are specified by calibrating the sample size and final decision rule to: a) achieve a high frequentist power and high minimum (or average) Bayesian positive predictive value of a significant result in children; or b) ensure that a final decision to adopt (abandon) drug E in children is always associated with a minimum positive (negative) predictive value. Operating characteristics of our Bayesian designs are evaluated and compared with those of a recently proposed hybrid approach (Hlavin et al. Statistics in Medicine 2016; 35: 2117) where the sample size and significance level of a frequentist confirmatory trial in children are set to achieve a high frequentist power and high average positive predictive value of a significant result in children.
Nicky Best, Dawn Edwards
A case study using Bayesian methods to leverage existing clinical efficacy data in paediatric trials
Recently there has been increased regulatory interest in partial extrapolation of adult efficacy information to paediatrics populations to reduce data collection requirements in children. In this talk we will present a case study describing plans to use partial extrapolation of adult efficacy data from a phase III trial of an experimental drug in adolescents with a respiratory disease. We will demonstrate how adult data on the treatment difference for the endpoint of interest can be included via an informative prior distribution to increase the probability of success of the study in adolescents and the precision of the estimated treatment difference. A method which incorporates dynamic borrowing will be used to define the level of extrapolation using a 2-step approach whereby information from the adult data is first incorporated into a prior distribution before being integrated with the data from the adolescent population. We propose a 3-component weighted robust mixture prior with the informative components based on (1) the adult efficacy data, (2) rescaled adult efficacy data to reflect the expected response for the adolescent population, and (3) a flat component to ensure that, in the event the adolescent and adult data are in clear conflict, the latter will have minimal influence on the posterior distribution of the treatment difference, thus also preventing excessive inflation of type 1 error. We will present results of a simulation study investigating operating characteristics for different choices of success criteria and prior weights.
Peter Milligan
Utilizing a Quantitative Framework to support Extrapolation
Peter A Milligan on behalf of the EFPIA MID3 Workgroup
The 2016 white paper on Good Practices in Model-Informed Drug Discovery and Development: Practice, Application, and Documentation (1) defines Model Informed Drug Discovery and Development (MID3) as a ‘‘quantitative framework for prediction and extrapolation, centred on knowledge and inference generated from integrated models of compound, mechanism and disease level data and aimed at improving the quality, efficiency and cost effectiveness of decision making’’.
MID3 in its simplest form embodies using ‘‘fit-for-purpose’’ mathematical models, implemented according to good practices, in order to enhance the extraction of inference from both existing information and data emanating from ongoing experiments. As the underpinning foundations for MID3 are based on robust scientific principles derived from pharmacological, physiological, and pathological processes (the domain sciences), MID3 can more effectively support translation across, and extrapolation beyond, the direct inference obtained from standard descriptive methods applied to experimental data.
Conversion of the current knowledge captured within the ‘‘fit-for-purpose’’ model into inference requires a prediction based either on particular model parameter estimates or utilizing values generated through simulation. Predictions can either be interpolative or extrapolative with respect to available evidence and the intended purpose.
Extrapolations beyond current experience often provide the greatest value to pharmaceutical companies. The recent EMA concept paper on The Extrapolation of Efficacy and Safety Data in Medicine Development (2) identified the approaches utilized in MID3 as part of the extrapolation concept and for inclusion in an extrapolation plan. The use of extrapolations emanating from an appropriate quantitative framework to bridge efficacy and safety in special populations was discussed in the 2011 joint workshop (3) with some of the resultant proposals subsequently published in greater detail (4). Replacement of direct experimental evidence (including all or part of a clinical trial) in a development program is conceptually ‘‘permissible’’ but considered to be of ‘‘high regulatory impact’’ (5) necessitating substantive a priori discussions with the regulatory agencies to characterize the context of use for any resultant extrapolations.
An introductory course giving an overview of the pharmaceutical industry and the drug development process as a whole, aimed at those with 1-3 years' experience. It comprises of six 2-day sessions covering a range of topics including Research and Development, Toxicology, Data Management and the Role of a CRO, Clinical Trials, Reimbursement, and Marketing.
This is an interactive online training workshop providing an in-depth review of the estimand framework as laid out by ICH E9(R1) addendum with inputs from estimand experts, case studies, quizzes and opportunity for discussions. You will develop an estimand in a therapeutic area of interest to your company. In an online break-out room, you will join a series of team discussions to implement the estimand framework in a case study, aligning estimands, design, conduct, analysis, (assumptions + sensitivity analyses) to the clinical objective and therapeutic setting.
Webinar dedicated to the topic of dissolution of oral solid dosage forms; opportunity to hear from statisticians working in the CMC field, with open question and answers. In addition, the CMC Statistical Network Europe special interest group will discuss advocacy opportunities, have your say to contribute to the future direction.
Initially a brief introduction to AI will be given, followed by a presentation of various applications in medicinal product lifecycle. Several aspects will be discussed including performance, theoretical and practical conditions and also regulatory guidelines.
Our monthly webinar explores examples of innovative data visualisations relevant to our day to day work. Each month a new dataset is provided from a clinical trial or other relevant example, and participants are invited to submit a graphic that communicates interesting and relevant characteristics of the data.
Who is this event intended for? Statisticians with an interest understanding dose-finding in oncology. What is the benefit of attending? Learn about the state of oncology dose finding, particularly in light of current FDA guidance.
The book club’s usual focus is to read and discuss professional development books. In this short format event you can more easily develop you career without the commitment of reading the whole book - simply listen to the 1-hour long podcast before joining the interactive session on 21 May.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
The Institute of Cancer Research are seeking to appoint a Research Group Leader to join The CRUK Clinical Trials and Statistics Unit at the ICR.
The BioMarin internship programme will enable students to gain valuable experience and knowledge of the processes and systems within BioMarin, whilst gaining an insight into the pharmaceutical/biotech industry.
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