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.

翻译：我们从数据分析师的角度讨论如何处理经调整的匹配间接比较(MAIC)的问题。我们采用了几种多变数据分析方法来评估MAIC对某一数据集是否适当。这些方法侧重于比较在提供简要统计数据的研究或综合数据(AD)和提供个人病人一级数据的研究(IPD)进行对比时所使用的基准变量。这些方法确定在采用MAIC方法时不可能采用数字解决办法的情况。这有助于避免产生误导性的结果。此外,我们注意到,由两个小组编写的两组MAIC分析报告的结果有时相互矛盾,这两个小组各自拥有自己的IPD,并利用另一小组公布的AD应用MAIC。我们表明,MAIC估计重量的内在属性可能是这一现象的一个促成因素。