Assessing the performance of population adjustment methods for anchored indirect comparisons: A simulation study.
Assessing the performance of population adjustment methods for anchored indirect comparisons: A simulation study.
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DOI：
10.1002/sim.8759
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发表时间：
2020-12-30
影响因子：
2
通讯作者：
Welton NJ
中科院分区：
文献类型：
--
作者：
Phillippo DM;Dias S;Ades AE;Welton NJ
标准网络Meta分析和间接比较联合收割机结合了来自多项研究的关注治疗的汇总数据，假设与治疗效应（效应修饰因子）相互作用的任何因素在人群中平衡。多水平网络Meta回归（ML-NMR）、匹配调整间接比较（MAIC）和模拟治疗比较（STC）等人群调整方法使用来自一项或多项研究的个体患者数据放宽了这一假设，并且在卫生技术评估和应用文献中越来越普遍。出于应用的例子和最近的两个应用程序的审查，我们进行了广泛的模拟研究，以评估这些方法的性能在一系列的情况下，各种故障的假设。我们研究了不同样本量、缺失效应修正因子、效应修正强度和共享效应修正因子假设的有效性、外推的有效性和研究间重叠的变化以及不同协变量分布和相关性的影响。ML-NMR和STC的表现相似，在满足必要假设时消除了偏倚。MAIC在几乎所有的模拟场景中表现不佳，与标准的间接比较相比，甚至可能增加偏倚，这引起了严重的关注。所有方法都会在缺少效应修饰符时产生偏差，这突出了在分析之前仔细选择潜在效应修饰符的必要性。当包括所有效应修饰剂时，ML-NMR和STC是用于群体调整的稳健技术。ML-NMR提供了比MAIC和STC更多的优势，包括扩展到更大的治疗网络，并在任何目标人群中产生估计，使其成为各种情况下有吸引力的选择。
Standard network meta‐analysis and indirect comparisons combine aggregate data from multiple studies on treatments of interest, assuming that any factors that interact with treatment effects (effect modifiers) are balanced across populations. Population adjustment methods such as multilevel network meta‐regression (ML‐NMR), matching‐adjusted indirect comparison (MAIC), and simulated treatment comparison (STC) relax this assumption using individual patient data from one or more studies, and are becoming increasingly prevalent in health technology appraisals and the applied literature. Motivated by an applied example and two recent reviews of applications, we undertook an extensive simulation study to assess the performance of these methods in a range of scenarios under various failures of assumptions. We investigated the impact of varying sample size, missing effect modifiers, strength of effect modification and validity of the shared effect modifier assumption, validity of extrapolation and varying between‐study overlap, and different covariate distributions and correlations. ML‐NMR and STC performed similarly, eliminating bias when the requisite assumptions were met. Serious concerns are raised for MAIC, which performed poorly in nearly all simulation scenarios and may even increase bias compared with standard indirect comparisons. All methods incur bias when an effect modifier is missing, highlighting the necessity of careful selection of potential effect modifiers prior to analysis. When all effect modifiers are included, ML‐NMR and STC are robust techniques for population adjustment. ML‐NMR offers additional advantages over MAIC and STC, including extending to larger treatment networks and producing estimates in any target population, making this an attractive choice in a variety of scenarios.
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