Abstract
Propensity score is widely used to estimate treatment effects in observational studies. The covariate adjustment using propensity score is the most straightforward method in the literature of causal inference. In this article, we estimate the survival treatment effect with covariate adjustment using propensity score in the semiparametric accelerated failure time model. We establish the asymptotic properties of the proposed estimator by simultaneous estimating equations. We conduct simulation studies to evaluate the finite sample performance of the proposed method. A real data set from the German Breast Cancer Study Group is analyzed to illustrate the proposed method.
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The authors are grateful for the valuable comments and suggestions from the editor, associate editor and two anonymous referees which drastically improved the article.
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Supported by the National Natural Science Foundation of China (Grant Nos. 11501578 and 11701571) and the Fundamental Research Funds for the Central Universities (Grant No. 31512111206)
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Cao, Y.X., Zhang, X.C. & Yu, J.C. Estimating Survival Treatment Effects with Covariate Adjustment Using Propensity Score. Acta. Math. Sin.-English Ser. 38, 2057–2068 (2022). https://doi.org/10.1007/s10114-022-0508-9
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DOI: https://doi.org/10.1007/s10114-022-0508-9
Keywords
- Accelerated failure time model
- covariate adjustment
- observational study
- propensity score
- simultaneous estimating equations