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Activity Number: 126
Type: Contributed
Date/Time: Monday, August 4, 2014 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #312934
Title: More Efficient Inference of Marginal Structural Cox Models with Case-Cohort Sampling
Author(s): Hana Lee*+ and Michael G. Hudgens and Jianwen Cai
Companies: and University of North Carolina and University of North Carolina at Chapel Hill
Keywords: Case-Cohort Study ; Causal Inference ; Cox Models ; Marginal Structural Models ; Survival Analysis ; Multiple Imputation
Abstract:

Recently Cole et al. (2012) considered case-cohort design in marginal structural Cox models (MSCM) analysis as a cost-efficient approach to obtain causal effect of (a function of) treatment from longitudinal observational studies in the presence of confounding. Lee et al. (2014) developed asymptotic distribution theory of the hazard ratio parameter estimate in the MSCM using the martingale technique, and proposed new variance estimators under the full and the case-cohort settings. Numerical studies showed that the proposed variance estimators can perform better than the robust variance estimator (Lin and Ying, 1993), which is typically used in most MSCM analysis, if subcohort size is small. However, compared to the full cohort MSCM analysis, efficiency loss is inevitable in the case-cohort MSCM analysis even when the martingale-based variance estimator is employed. In this talk, I will discuss a new method to improve efficiency of the case-cohort MSCM analysis which aims to evaluate the causal effect of treatment on a time to event outcome. Our method seeks to improve the efficiency by implementing multiple imputation (MI) as the design induces missing data.


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