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Activity Number: 174 - Biomarkers and Endpoint Validation
Type: Contributed
Date/Time: Monday, July 30, 2018 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract #329407
Title: Inference on Treatment Effect Modification by Marker Response in a Baseline Surrogate Measure Three-Phase Sampling Design
Author(s): Michal Juraska* and Ying Huang and Peter Gilbert
Companies: Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center
Keywords: biomarker; dengue vaccine; principal stratification; principal surrogate endpoint; three-phase sampling design; treatment effect modification

An objective in randomized clinical trials is the evaluation of "principal surrogates," which studies how the treatment effect on a clinical endpoint varies over principal strata subgroups defined by an intermediate response outcome under both or one of the treatment assignments; the latter effect modification estimand has been termed the marginal causal effect predictiveness (mCEP) curve. This objective was addressed in two randomized placebo-controlled Phase 3 dengue vaccine trials for an antibody response biomarker whose sampling design rendered previously developed inferential methods highly inefficient due to the three-phase sampling design where the biomarker was measured in a case-cohort sample and a key baseline auxiliary strongly associated with the biomarker (the "baseline surrogate measure") was only measured in a further sub-sample. We propose a novel approach to estimation and inference about the mCEP curve in such three-phase sampling designs that avoids the restrictive "placebo structural risk" modeling assumption common to past methods and that further improves robustness by the use of nonparametric kernel smoothing for biomarker density estimation.

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

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