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Activity Number: 625 - Personalized/Precision Medicine II
Type: Contributed
Date/Time: Thursday, August 2, 2018 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #327137 Presentation
Title: A Non-Parametric Statistical Test of Null Treatment Effect in Sub-Populations
Author(s): Lin Taft* and Changyu Shen
Companies: GSK and Beth Israel Deaconess Medical Center, Harvard Medical School
Keywords: Heterogeneity; Nonparametric; Randomized Trial; Stochastic Search

Randomized clinical trials are designed to estimate the average treatment effect (ATE), it is important to detect if heterogeneity exists in the treatment responses. We propose a method to test the hypothesis that the treatment has no effect on each of the smallest sub-populations, which are defined by discrete baseline covariates using randomized trial data. Our approach is nonparametric, which generates the null distribution of the test statistic by the permutation principle. A key innovation of our method is that stochastic simulation is built into the test statistic to detect signals that may not be linearly related to the multiple covariates. This is important because in many real clinical problems, the treatment effect is not linearly correlated with relevant baseline characteristics. We applied the method to a real randomized study that compared the Implantable Cardioverter Defibrillator (ICD) with conventional medical therapy in reducing total mortality in a low ejection fraction population. Simulations and power calculations were performed to compare the proposed test with existing methods.

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

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