  We discuss how to handle matching-adjusted indirect comparison (MAIC) from a
data analyst's perspective. We introduce several multivariate data analysis
methods to assess the appropriateness of MAIC for a given data set. These
methods focus on comparing the baseline variables used in the matching from a
study that provides the summary statistics, or aggregated data (AD) and a study
that provides individual patient level data (IPD). The methods identify
situations when no numerical solutions are possible with the MAIC method. This
helps to avoid misleading results being produced. Moreover, it has been
observed that sometimes contradicting results are reported by two sets of MAIC
analyses produced by two teams, each having their own IPD and applying MAIC
using the AD published by the other team. We show that an intrinsic property of
the MAIC estimated weights can be a contributing factor for this phenomenon.
