The Matching-Adjusted Indirect Comparison method (MAIC) is a recent methodology that allows to perform indirect comparisons between two drugs assessed in two different studies, where individual patients data are available in only one of the two studies, the data of the other one being available in an aggregate format only. In this work, we have assessed the properties of the MAIC method and compared, through simulations, several ways of practical implementation of the method. We conclude that it is more efficient to match the treatment arms separately (match the two drugs to compare on one hand, and the control arms on the other hand) and use the Lasso technique to select the covariates for the matching step is better than matching a maximal set of covariates.
Keywords: Cox model; hazard ratio; indirect comparison; matching; propensity score; time-to-event.
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