Matching-Adjusted Indirect Comparisons: A New Tool for Timely Comparative Effectiveness Research
Matching-Adjusted Indirect Comparisons: A New Tool for Timely Comparative Effectiveness Research
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DOI：
10.1016/j.jval.2012.05.004
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发表时间：
2012-09-01
期刊：
影响因子：
4.5
通讯作者：
Wu, Eric Q.
中科院分区：
文献类型：
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作者：
Signorovitch, James E.;Sikirica, Vanja;Wu, Eric Q.
目的：在缺乏头对头随机试验的情况下，可以对不同试验的治疗进行间接比较。然而，这些分析可能会因患者群体的交叉试验差异、对建模假设的敏感性以及结果测量定义的差异而产生偏差。本研究的目的是证明如何将一种治疗试验的个体患者数据（IPD）纳入间接比较，可以解决仅基于汇总数据的分析中出现的一些局限性。方法：匹配调整间接比较 (MAIC) 使用一种治疗试验的 IPD 来匹配另一种治疗试验报告的基线汇总统计数据。匹配后，通过使用类似于倾向评分加权的方法，在平衡的试验人群中比较治疗结果。通过回顾不同治疗领域已发表的 MAIC 来说明该方法。对注意力缺陷/多动障碍的新颖分析进一步证明了该方法的适用性。与仅使用已发布的汇总数据的间接比较相比，讨论了 MAIC 的优点和局限性。结果：选择示例应用程序来说明仅基于汇总数据的间接比较如何受到患者群体的交叉试验差异、结果测量定义的差异以及对建模假设的敏感性的限制。 IPD 和 MAIC 的使用被证明可以通过减少或消除观察到的交叉试验差异来解决所选示例中的这些限制。与非随机治疗组的任何比较一样，MAIC 的一个重要假设是，不存在可能混淆结果比较的未观察到的交叉试验差异。结论：间接治疗比较可能受到跨试验差异的限制。通过将 IPD 与已发布的汇总数据相结合，MAIC 可以减少观察到的跨试验差异，并为决策者提供及时的比较证据。
Objective: In the absence of head-to-head randomized trials, indirect comparisons of treatments across separate trials can be performed. However, these analyses may be biased by cross-trial differences in patient populations, sensitivity to modeling assumptions, and differences in the definitions of outcome measures. The objective of this study was to demonstrate how incorporating individual patient data (IPD) from trials of one treatment into indirect comparisons can address several limitations that arise in analyses based only on aggregate data. Methods: Matching-adjusted indirect comparisons (MAICs) use IPD from trials of one treatment to match baseline summary statistics reported from trials of another treatment. After matching, by using an approach similar to propensity score weighting, treatment outcomes are compared across balanced trial populations. This method is illustrated by reviewing published MAICs in different therapeutic areas. A novel analysis in attention deficit/hyperactivity disorder further demonstrates the applicability of the method. The strengths and limitations of MAICs are discussed in comparison to those of indirect comparisons that use only published aggregate data. Results: Example applications were selected to illustrate how indirect comparisons based only on aggregate data can be limited by cross-trial differences in patient populations, differences in the definitions of outcome measures, and sensitivity to modeling assumptions. The use of IPD and MAIC is shown to address these limitations in the selected examples by reducing or removing the observed cross-trial differences. An important assumption of MAIC, as in any comparison of nonrandomized treatment groups, is that there are no unobserved cross-trial differences that could confound the comparison of outcomes. Conclusions: Indirect treatment comparisons can be limited by cross-trial differences. By combining IPD with published aggregate data, MAIC can reduce observed cross-trial differences and provide decision makers with timely comparative evidence.