Doubly weighted mean score estimating functions with a partially observed effect modifier

Meaghan S. Cuerden, Liqun Diao, Cecilia A. Cotton and Richard J. Cook

Communications in Statistics - Theory and Methods, 2024, vol. 53, issue 11, 3899-3919

Abstract: Effect modification plays a central role in stratified medicine, of which the goal is often to find biomarker profiles that identify individuals who benefit from a treatment of interest. We consider the problem of causal inference regarding the effect modifying role of a biomarker, which is only available for some individuals in an observational study. We develop inverse probability weighted mean score estimating functions with one weight to account for confounding and a second weight for the missing data process. An iterative approach is described for solving the equations in the spirit of the expectation-maximization algorithm, and large sample properties of the resulting estimator are developed. Simulation studies are conducted to compare the proposed method with a doubly weighted complete case analysis and a propensity score weighted multiple imputation approach. An application to a study of the effect of a biologic therapy on inflammation in a rheumatology cohort is given for illustration.

Date: 2024
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DOI: 10.1080/03610926.2023.2166790

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