Affiliation:
1. Department of Statistics and Data Science, The Wharton School University of Pennsylvania Philadelphia Pennsylvania USA
Abstract
AbstractIn a recent issue of the Journal; Remiro‐Azócar et al. introduce a new method to adjust for population difference between two trials; when the individual patient data (IPD) are only accessible for one study. The proposed method generates the covariate data for the trial without IPD; then using a G‐computation approach to transport information about the treatment effect from the other study with IPD to this trial. The authors advocate the use of G‐computation over matching‐adjusted indirect comparison because (i) the former allows for “useful extrapolation” when there is poor case‐mix overlap between populations; and (ii) nonparametric; data‐adaptive methods can be used to reduce the risk of (outcome) model misspecification. In this commentary; we provide a different perspective from these arguments. Despite certain disagreements; we believe that the proposed data generation approaches can open new and interesting research directions for population adjustment methodology in the future.
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