|
Abstract:
|
Complementary features of randomized clinical trials (RCTs) and observational studies can be used jointly to estimate the average treatment effect of a target population. Based on this idea, we propose a calibration weighting estimator that enforces the covariate balance between the RCT and observational study, therefore improves the generalizability of the trial-based estimator. Exploiting semiparametric efficiency theory, we propose a doubly robust augmented calibration weighting estimator that achieves the efficiency bound derived under the identification assumptions. A nonparametric sieve method is provided as an alternative to the parametric approach, which enables the robust approximation of the nuisance functions and data-adaptive selection of calibration variables. We establish asymptotic results and confirm the finite sample performances of the proposed estimators by simulation experiments and an application on the estimation of the treatment effect of adjuvant chemotherapy.
|
