Integrative analysis of randomized clinical trials with real world evidence studies
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We leverage the complementing features of randomized clinical trials (RCTs) and real world evidence (RWE) to estimate the average treatment effect of the target population. First, we propose a calibration weighting estimator that uses only covariate information from the RWE study. Because this estimator enforces the covariate balance between the RCT and RWE study, the generalizability of the trial-based estimator is improved. We further propose a doubly robust augmented calibration weighting estimator that achieves the semiparametric efficiency bound derived under the identification assumptions when the nuisance models are correctly specified. A data-adaptive nonparametric sieve method is provided as an alternative to the parametric approach. The sieve method guarantees good approximation of the nuisance models. We establish asymptotic results under mild regularity conditions, and confirm the finite sample performances of the proposed estimators by simulation experiments. When the treatment and outcome information is also available from the RWE study, we borrow its predictive power to improve the estimation of the nuisance functions under the outcome mean function transportability assumption. We apply our proposed methods to estimate the effect of adjuvant chemotherapy in early-stage resected non-small-cell lung cancer integrating data from a RCT and a sample from the National Cancer Database.
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