Estimating survival treatment effects with covariate adjustment using propensity score. (English) Zbl 1503.62085
Summary: 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.
MSC:
| 62N05 | Reliability and life testing |
| 62D20 | Causal inference from observational studies |
| 62N01 | Censored data models |
| 62N02 | Estimation in survival analysis and censored data |
Keywords:
accelerated failure time model; covariate adjustment; observational study; propensity score; simultaneous estimating equationsReferences:
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