Missing Data Handling in the Application of Matching-Adjusted Indirect Comparison

In support of Health Technology Assessment submission, we often need to conduct indirect treatment comparisons (ITC). One common type of ITC is population-adjusted indirect comparisons (Phillippo et al. in NICE DSU technical support document 18: methods for population-adjusted indirect comparisons in submissions to NICE. NICE Decision Support Unit, 2016), in which individual patient data in one trial and aggregate data in the other trial are used to adjust for the difference in the distributions of covariates (prognostic factors or effect modifiers) that influence the outcome. The most popular PAIC method is the Matching-Adjusted Indirect Comparison (MAIC) (Signorovitch et al. in Pharmacoeconomics 28:935-945, 2010). However, the literature lacks guidance on how to handle missing data in the application of MAIC. In this paper, we propose some weighting-based methods to handle missing data in the outcome variable and/or covariates when applying MAIC. These weights can be expressed as products of the inverse probability of not missing the outcomes and weights that account for the difference in baseline characteristics. The proposed methods fit seamlessly into the original MAIC framework and obtain treatment effect estimates based on weighted difference between IPD and AgD. Extensive simulation studies are conducted to evaluate the performance of these proposed methods.