Esnault Cyril, Baschet Louise, Barbet Vanessa, Chenuc Gaëlle, Pérol Maurice, Thokagevistk Katia, Pau David, Monnereau Matthias, Bosquet Lise, Filleron Thomas
Roche SAS, Boulogne-Billancourt, France.
Horiana, Bordeaux, France.
BMC Med Res Methodol. 2025 Mar 1;25(1):57. doi: 10.1186/s12874-025-02500-w.
Matching Adjusted Indirect Comparison (MAIC) is a statistical method used to adjust for potential biases when comparing treatment effects between separate data sources, with aggregate data in one arm, and individual patients data in the other. However, acceptance of MAIC in health technology assessment (HTA) is challenging because of the numerous biases that can affect the estimates of treatment effects - especially with small sample sizes, increasing the risk of convergence issues. We suggest statistical approaches to address some of the challenges in supporting evidence from MAICs, applied to a case study.
The proposed approaches were illustrated with a case study comparing an integrated analysis of three single-arm trials of entrectinib with the French standard of care using the Epidemio-Strategy and Medical Economics (ESME) Lung Cancer Data Platform, in metastatic ROS1-positive Non-Small Cell Lung Cancer (NSCLC) patients. To obtain convergent models with balanced treatment arms, a transparent predefined workflow for variable selection in the propensity score model, with multiple imputation of missing data, was used. To assess robustness, multiple sensitivity analyses were conducted, including Quantitative Bias Analyses (QBA) for unmeasured confounders (E-value, bias plot), and for missing at random assumption (tipping-point analysis).
The proposed workflow was successful in generating satisfactory models for all sub-populations, that is, without convergence problems and with effectively balanced key covariates between treatment arms. It also gave an indication of the number of models tested. Sensitivity analyses confirmed the robustness of the results, including to unmeasured confounders. The QBA performed on the missing data allowed to exclude the potential impact of the missing data on the estimate of comparative effectiveness, even though approximately half of the ECOG Performance Status data were missing.
To the best of our knowledge, we present the first in-depth application of QBA in the context of MAIC. Despite the real-world data limitations, with this MAIC, we show that it is possible to confirm the robustness of the results by using appropriate statistical methods.
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匹配调整间接比较（MAIC）是一种统计方法，用于在比较不同数据源的治疗效果时调整潜在偏差，其中一个组采用汇总数据，另一个组采用个体患者数据。然而，MAIC在卫生技术评估（HTA）中的接受度具有挑战性，因为存在许多可能影响治疗效果估计的偏差——尤其是在样本量较小时，这会增加收敛问题的风险。我们提出了一些统计方法来应对支持MAIC证据时遇到的一些挑战，并将其应用于一个案例研究。
通过一个案例研究来说明所提出的方法，该案例研究使用流行病学策略和医学经济学（ESME）肺癌数据平台，比较了恩曲替尼三项单臂试验的综合分析与法国转移性ROS1阳性非小细胞肺癌（NSCLC）患者的标准治疗。为了获得具有平衡治疗组的收敛模型，使用了一种用于倾向评分模型中变量选择的透明预定义工作流程，并对缺失数据进行多次插补。为了评估稳健性，进行了多项敏感性分析，包括对未测量混杂因素的定量偏差分析（QBA）（E值、偏差图）以及对随机缺失假设的分析（临界点分析）。
所提出的工作流程成功地为所有亚组生成了令人满意的模型，即没有收敛问题，且治疗组之间的关键协变量有效平衡。它还给出了测试模型数量的指示。敏感性分析证实了结果的稳健性，包括对未测量混杂因素的稳健性。对缺失数据进行的QBA允许排除缺失数据对比较有效性估计的潜在影响，尽管大约一半的ECOG体能状态数据缺失。
据我们所知，我们首次在MAIC背景下深入应用了QBA。尽管存在真实世界数据的局限性，但通过这种MAIC，我们表明使用适当的统计方法可以确认结果的稳健性。
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