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Activity Number:
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240
- Topics in Multiplicity and Control of False Discovery Rate
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Type:
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Contributed
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Date/Time:
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Monday, July 29, 2019 : 2:00 PM to 3:50 PM
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Sponsor:
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Biopharmaceutical Section
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Abstract #304248
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Presentation
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Title:
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Credible Subgroups for Identifying Benefiting Populations with Time-To-Event Data
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Author(s):
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Duy Ngo* and Richard Baumgartner and Shahrul Mt-Isa and Dai Feng and Jie Chen and Joe Heyse and Patrick Schnell
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Companies:
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and Merck Research Laboratories and MSD and AbbVie and Merck Research Laboratories and Merck and Ohio State University
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Keywords:
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subgroup analysis;
survival data;
Bayesian;
restricted mean survival time;
risk benefit
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Abstract:
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The importance of demonstrating patient subgroups who benefit from a treatment in a clinical trial is increasingly recognized by regulators and health technology assessment agencies worldwide. Most proposed methods that currently addressed this issue only focus on treatment-covariate interactions and do not fully account for multiplicity. To overcome these limitations, we introduce the Bayesian credible subgroups survival analysis method to identify the baseline covariate profiles of patients who benefit from treatment. We estimate the treatment effect within the identified benefiting subgroup using log hazard ratio and restricted mean survival time (RMST). The advantages of our approach are that: (1) it does not require pre-specification of subgroups and work directly with the covariate space, and (2) it naturally makes statistical inferences from the full posterior distribution. We also investigate frequentist properties of this method and compare it to other methods such as Bayesian regression tree in a simulation study. In general, the nominal coverage has been preserved, however at lower sensitivity in some scenarios. Our methods applied to a case study of prostate carcinoma.
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Authors who are presenting talks have a * after their name.
