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Activity Number: 399 - Pragmatic Innovations in Clinical Trial Designs: Key to Real Applications
Type: Invited
Date/Time: Wednesday, August 10, 2022 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract #320350
Title: Propensity-Score-Based Meta-Analytic Predictive Prior for Incorporating Real-World and Historical Data
Author(s): Jianchang Lin* and Meizi Liu and Veronica Bunn and Bradley Hupf and Junjing Lin
Companies: Takeda Pharmaceuticals and University of Chicago and Takeda Pharmaceuticals and Takeda Pharmaceuticals and Takeda Pharmaceuticals
Keywords: Bayesian borrowing; Effective sample size; Meta-analytic-predictive prior; Propensity score; Real-world data
Abstract:

As the availability of historical data and real-world data sources (e.g., EHRs, claims data, registries) has rapidly surged in recent years, there is an increasing interest and need from drug developers and health authorities to leverage all available information to reduce patient burden and accelerate both drug development and regulatory decision making. Several Bayesian methods for incorporating historical information via a prior distribution have been proposed, e.g. (modified) power prior, (robust) meta-analytic predictive prior. In this presentation, we will discuss some recent advancements and new methodologies for external data borrowing, e.g. Bayesian semiparametric meta-analytic-predictive prior (BaSe-MAP) [Hupf et al, 2021, SIM], Propensity Score integrated Meta-Analytic Predictive (PS-MAP) prior [Liu et al, 2021, SIM]. Overall, these methods provide a more flexible and robust approach to integrate historical data in the design and analysis of clinical trials. Simulation studies and illustration examples with a proof-of-concept study are used to compare the performance of existing commonly used methods with the proposed methods.


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