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

Author

Listed:
  • Meaghan S. Cuerden
  • Liqun Diao
  • Cecilia A. Cotton
  • Richard J. Cook

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.

Suggested Citation

  • Meaghan S. Cuerden & Liqun Diao & Cecilia A. Cotton & Richard J. Cook, 2024. "Doubly weighted mean score estimating functions with a partially observed effect modifier," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 53(11), pages 3899-3919, June.
    • Handle: RePEc:taf:lstaxx:v:53:y:2024:i:11:p:3899-3919
    DOI: 10.1080/03610926.2023.2166790
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