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Activity Number: 617 - Indirect Comparisons of Treatment Effects for Clinical Regulatory and Health Economic Evaluations
Type: Topic Contributed
Date/Time: Thursday, August 2, 2018 : 8:30 AM to 10:20 AM
Sponsor: Biopharmaceutical Section
Abstract #329906
Title: Retrospective Matched-Pairs Analysis for Clinical Trial Patient Level Data: a Simulation Study and General Considerations
Author(s): Bingxia Wang* and Chenchen Ma and Yanyan Zhu and Guohui Liu
Companies: Takeda Pharmaceuticals Inc. and and Takeda Pharmaceuticals and Takeda Pharmaceuticals Inc
Keywords: matched-pair analysis; clinical trial ; propensity score; simulation

The clinical data emerge rapidly in both individual patient level data and aggregated data for indirect comparison due to increasing demand from payers, regulatory acceptance such as label expansion and optimizing trial development. Matched-pair analysis is one of the indirect comparison approaches offering compelling evidence of the relative effect estimates. In this talk, we will provide the overview of the statistical methodology of matched-pair analysis including covariates selection, propensity score matching and balance check after matching. A simulation study will be performed through individual patient level data from a published clinical trial. The comparison of propensity score matching with other approaches will also be discussed to demonstrate that the main advantage of the matched-pair analysis is to minimize bias.

Authors who are presenting talks have a * after their name.

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