Department of Statistics
North Carolina State University
Integrative Analysis of Randomized Clinical Trials with Real-World Studies
We leverage the complementing features of randomized clinical trials (RCT) 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 can be applied in the event that treatment and outcome information is also available from the RWE study. This estimator achieves the semiparametric efficiency bound derived under the identification and outcome mean function transportability 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. 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.
Friday, October 18, 2019, 11:30 a.m. BLOC 113