A Powerful Approach to the Study of Moderate Effect Modification in Observational Studies

• Activity Number: 482 - Causal Inference and Related Methods
• Type: Contributed
• Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Kwonsang Lee* and Dylan Small and Paul Rosenbaum
• Companies: Harvard University and University of Pennsylvania and University of Pennsylvania
• Keywords: causal effects; causal inference; design sensitivity; epidemiology; sensitivity analysis; testing twice

Abstract

Effect modification occurs when the magnitude or stability of a treatment effect varies as a function of an observed covariate. Generally, larger and more stable treatment effects are insensitive to larger biases from unmeasured covariates, so a causal conclusion may be considerably firmer if effect modification is noted when it occurs. We propose a new method, called the submax-method, that combines exploratory and confirmatory efforts to discover effect modification. It uses the joint distribution of test statistics that split the data in various ways based on observed covariates. The method splits the population L times into two subpopulations, computing a test statistic from each subpopulation, and appends the test statistic for the whole population, making 2L+1 test statistics in total. The submax-method achieves the highest design sensitivity and the highest Bahadur efficiency of its component tests. Moreover, the form of the test is sufficiently tractable that its large sample power may be studied analytically. Using data from the NHANES I Epidemiologic Follow-Up Survey, an observational study of the effects of physical activity on survival is used to illustrate the method.