Title: Use of Historical Data in Clinical Trial: An Evidence Synthesis Approach
Speakers: Satrajit Roychoudhury (Pfizer Inc) and Sebastian Weber (Novartis)
A Bayesian approach provides the formal framework to incorporate external information into the statistical analysis of a clinical trial. There is an intrinsic interest of leveraging all available information for an efficient design and analysis. This allows trials with smaller sample size or with unequal randomization. Examples include early phases drug development, occasionally in phase III trial, and special areas such as medical devices, orphan indications and extrapolation in pediatric studies. Recently, 21st Century Cure Act and PUDUFA VI encourage the use of relevant historical data for efficient design. An appropriate statistical method in this context needs to leverage “borrowing” of information while considering the heterogeneity between historical and current trial. In this KOL lecture, we'll cover the statistical frameworks to incorporate trial external evidence with real life example.
We will introduce the meta-analytic predictive (MAP) framework for borrowing historical data. The MAP approach is based on Bayesian hierarchical model which combines the evidence from different sources. It provides a prediction for the current study based on the available information while accounting for inherent heterogeneity in the data. This approach can be used widely in different applications of clinical trial. We will focus on the implementation of the MAP approach in clinical trial. Different applications will be demonstrated using the R package RBesT, the R Bayesian evidence synthesis tools, which are freely available from CRAN.
Slides will be stored in http://www.bayesianscientific.org/kol-lecture-series/
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Webinar ID: 796 327 928