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In the presence of heterogeneity between the randomized controlled trial(RCT) participants and the target population, evaluating the treatment effectsolely based on the RCT often leads to biased quantification of the real-worldtreatment effect. To address the problem of lack of generalizability for thetreatment effect estimated by the RCT sample, we leverage observational studieswith large samples that are representative of the target population. This paperconcerns evaluating treatment effects on survival outcomes for a targetpopulation and considers a broad class of estimands that are functionals oftreatment-specific survival functions, including differences in survivalprobability and restricted mean survival times. Motivated by two intuitive butdistinct approaches, i.e., imputation based on survival outcome regression andweighting based on inverse probability of sampling, censoring, and treatmentassignment, we propose a semiparametric estimator through the guidance of theefficient influence function. The proposed estimator is doubly robust in thesense that it is consistent for the target population estimands if either thesurvival model or the weighting model is correctly specified, and is locallyefficient when both are correct. In addition, as an alternative to parametricestimation, we employ the nonparametric method of sieves for flexible androbust estimation of the nuisance functions and show that the resultingestimator retains the root-n consistency and efficiency, the so-called itrate-double robustness. Simulation studies confirm the theoretical propertiesof the proposed estimator and show it outperforms competitors. We apply theproposed method to estimate the effect of adjuvant chemotherapy on survival inpatients with early-stage resected non-small lung cancer.

Doubly robust estimators for generalizing treatment effects on survival outcomes from randomized controlled trials to a target population