Indirect comparisons of treatment-specific outcomes across separate studies often inform decision making in the absence of head-to-head randomized comparisons. Differences in baseline characteristics between study populations may introduce confounding bias in such comparisons. Matching-adjusted indirect comparison (MAIC) (Pharmacoeconomics 28 (2010) 935–945) has been used to adjust for differences in observed baseline covariates when the individual patient-level data (IPD) are available for only one study and aggregate data (AGD) are available for the other study. The approach weights outcomes from the IPD using estimates of trial selection odds that balance baseline covariates between the IPD and AGD. With the increasing use of MAIC, there is a need for formal assessments of its statistical properties. In this paper we formulate identification assumptions for causal estimands that justify MAIC estimators. We then examine large sample properties and evaluate strategies for estimating standard errors without the full IPD from both studies. The finite-sample bias of MAIC and the performance of confidence intervals based on different standard error estimators are evaluated through simulations. The method is illustrated through an example comparing placebo arm and natural history outcomes in Duchenne muscular dystrophy.

中文翻译：

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匹配调整后的间接比较的统计效果：使用总体外部控制数据估算治疗效果

在单独的研究中，针对治疗特定结果的间接比较通常会在缺乏直接的随机比较的情况下为决策提供依据。研究人群之间基线特征的差异可能会在此类比较中引起混淆。匹配调整间接比较（MAIC）（药物 经济学28（2010）935–945）已用于仅在一项研究中获得单个患者水平数据（IPD）而在另一项研究中获得聚合数据（AGD）时，对观察到的基线协变量之间的差异进行调整。该方法使用平衡IPD和AGD之间基线协变量的试验选择几率的估计值来加权IPD的结果。随着MAIC的使用越来越多，需要对其统计属性进行正式评估。在本文中，我们为因果估计制定识别假设，以证明MAIC估计是正确的。然后，我们检查了大样本属性并评估了在没有两项研究都具有完整IPD的情况下估计标准误的策略。通过仿真评估了MAIC的有限样本偏差和基于不同标准误差估计量的置信区间的性能。通过比较杜兴氏肌营养不良症的安慰剂组和自然史结局的实例说明了该方法。