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This commentary regards a recent simulation study conducted by Aouni,Gaudel-Dedieu and Sebastien, evaluating the performance of different versionsof matching-adjusted indirect comparison (MAIC) in an anchored scenario with acommon comparator. The simulation study uses survival outcomes and the Coxproportional hazards regression as the outcome model. It concludes that usingthe LASSO for variable selection is preferable to balancing a maximal set ofcovariates. However, there are no treatment effect modifiers in imbalance inthe study. The LASSO is more efficient because it selects a subset of themaximal set of covariates but there are no cross-study imbalances in effectmodifiers inducing bias. We highlight the following points: (1) in the anchoredsetting, MAIC is necessary where there are cross-trial imbalances in effectmodifiers; (2) the standard indirect comparison provides greater precision andaccuracy than MAIC if there are no effect modifiers in imbalance; (3) while thetarget estimand of the simulation study is a conditional treatment effect, MAICtargets a marginal or population-average treatment effect; (4) in MAIC,variable selection is a problem of low dimensionality and sparsity-inducingmethods like the LASSO may be problematic. Finally, data-driven approaches donot obviate the necessity for subject matter knowledge when selecting effectmodifiers. R code is provided in the Appendix to replicate the analyses andillustrate our points.

Effect modification in anchored indirect treatment comparison: Comments on “Matching‐adjusted indirect comparisons: Application to time‐to‐event data”