Analysis of Randomized Clinical Trials with Integrated Information from Real-World Evidence Studies

• Activity Number: 582 - Integrating Information from Different Data Sources: Some New Developments
• Type: Topic Contributed
• Date/Time: Thursday, August 6, 2020 : 3:00 PM to 4:50 PM
• Sponsor: International Chinese Statistical Association
• Author(s): Shu Yang* and Lin Dong and Xiaofei Wang and Donglin Zeng and Jianwen Cai
• Companies: North Carolina State University and Wells fargo and Duke University and University of North Carolina at Chapel Hill and University of North Carolina at Chapel Hill
• Keywords: Causal effect generalization; Data integration; Doubly robustness; Semiparametricefficiency

Abstract

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. 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 an RCT and a sample from the National Cancer Database.