Abstract Number:
2284
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
jiaheng xie (1), Min Zhang (2)
Institutions:
(1) Department of Biostatistics, University of Michigan, Ann Arbor, (2) Tsinghua University, Beijing
Co-Author:
First Author:
Jiaheng Xie
Department of Biostatistics, University of Michigan
Presenting Author:
Abstract Text:
In randomized clinical trials, often the primary goal is to estimate the treatment effect. Robust covariate adjustment is a preferred statistical method since it improves efficiency and is robust to model misspecification. However, it is still underutilized in practice. One practical challenge is the missing covariates. Though missing covariates have been studied extensively, most of the existing work focuses on the relationship between outcome and covariates, with little on robust covariate adjustment for estimating treatment effect when covariates are missing. In this article, we recognize that the usual robust covariate adjustment could be directly generalized to the scenario when covariates are missing with the additional assumption that missingness is independent of treatment assignment. We also propose three different implementation strategies in order to handle the increased dimensionality in working models caused by missingness. Simulations and data application demonstrate the performance of proposed strategies. Practical recommendations are presented in the discussion.
Keywords:
imputation|missing covariates| randomized clinical trials|robust covariate adjustment| |
Sponsors:
Biometrics Section
Tracks:
Missing Data
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