Title: Strategies for improving the assessment of the probability of success in late stage drug development
Presenters: Lisa Hampson and Björn Holzhauer, Novartis Pharma AG, Basel
Abstract: There are several steps to confirming the safety and efficacy of a new medicine. A sequence of trials, each with its own objectives, is usually required. Quantitative risk metrics can be useful for informing decisions about whether a medicine should transition from one stage of development to the next. To obtain an estimate of the probability of regulatory approval, pharmaceutical companies may start with industry-wide success rates and then apply subjective adjustments to these to reflect program-specific information. However, this approach lacks transparency and fails to make full use of data from previous clinical trials. We describe a quantitative Bayesian approach for calculating the probability of success (PoS) at the end of phase II. It combines industry-wide success rates with internal clinical data and an assessment of risks beyond the pivotal studies.
In cases where there are differences between the phase IIb and future phase III trials due to a change in endpoint for example, we propose eliciting expert judgements to relate the existing data to the unknown quantities of interest. We discuss two approaches for establishing a multivariate distribution for several related efficacy treatment effects within the Sheffield Elicitation Framework (SHELF) and describe how they were applied to evaluate the PoS of the registrational program of an asthma drug.
Lisa is a member of the Advanced Methodology & Data Science group at Novartis based in Basel, Switzerland, where her role is to support the development and implementation of innovative statistical methods (... many of them Bayesian!). Prior to joining the pharmaceutical industry, Lisa was a Lecturer in Statistics at Lancaster University in the United Kingdom, and held a UK Medical Research Council (MRC) Career Development Award in Biostatistics. Her research interests are in clinical trials, including group sequential and adaptive designs, Bayesian approaches for leveraging external information in clinical trials and methods for quantitative decision-making.
Björn has developed drugs as a biostatistician in the respiratory, cardiovascular, diabetes and gastrointestinal disease areas for more than 15 years. In these areas he has been the project statistician for several cardiovascular, respiratory and other drugs, as well as for some machine learning projects. His research interests include estimands/missing data, Bayesian methods, time-to-event and count data, analysis of aggregate data, meta-analysis, and dose finding. He is also a member of the statistics team for the new Novartis probability of success framework.
Slides will be provided before the presentation at: http://www.bayesianscientific.org/kol-lecture-series/
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