A cautionary note on the use of G-computation in population adjustment

In 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.