School of Public Health (Shenzhen), Sun Yat-sen University, Room 215, Mingde Garden #6, 132 East Outer Ring Road, Pan-yu District, Guangzhou, Guangdong, China.
Department of Pharmaceutical and Health Economics, University of Southern California, 635 Downey Way, Verna & Peter Dauterive Hall (VPD) Suite 210, Los Angeles, CA, 90089-3333, USA.
BMC Med Res Methodol. 2020 Sep 29;20(1):241. doi: 10.1186/s12874-020-01124-6.
The objectives of the present study were to evaluate the performance of a time-to-event data reconstruction method, to assess the bias and efficiency of unanchored matching-adjusted indirect comparison (MAIC) methods for the analysis of time-to-event outcomes, and to propose an approach to adjust the bias of unanchored MAIC when omitted confounders across trials may exist.
To evaluate the methods using a Monte Carlo approach, a thousand repetitions of simulated data sets were generated for two single-arm trials. In each repetition, researchers were assumed to have access to individual-level patient data (IPD) for one of the trials and the published Kaplan-Meier curve of another. First, we compared the raw data and the reconstructed IPD using Cox regressions to determine the performance of the data reconstruction method. Then, we evaluated alternative unanchored MAIC strategies with varying completeness of covariates for matching in terms of bias, efficiency, and confidence interval coverage. Finally, we proposed a bias factor-adjusted approach to gauge the true effects when unanchored MAIC estimates might be biased due to omitted variables.
Reconstructed data sufficiently represented raw data in the sense that the difference between the raw and reconstructed data was not statistically significant over the one thousand repetitions. Also, the bias of unanchored MAIC estimates ranged from minimal to substantial as the set of covariates became less complete. More, the confidence interval estimates of unanchored MAIC were suboptimal even using the complete set of covariates. Finally, the bias factor-adjusted method we proposed substantially reduced omitted variable bias.
Unanchored MAIC should be used to analyze time-to-event outcomes with caution. The bias factor may be used to gauge the true treatment effect.
本研究的目的是评估一种生存数据重构方法的性能，评估无锚定匹配调整间接比较（MAIC）方法分析生存数据的偏倚和效率，并提出一种方法来调整当试验间可能存在遗漏混杂因素时无锚定 MAIC 的偏倚。
为了使用蒙特卡罗方法评估这些方法，我们生成了两个单臂试验的一千次重复模拟数据集。在每次重复中，假设研究人员可以访问一个试验的个体水平患者数据（IPD）和另一个试验的已发表的 Kaplan-Meier 曲线。首先，我们使用 Cox 回归比较原始数据和重构的 IPD，以确定数据重构方法的性能。然后，我们根据匹配的协变量的完整性，评估了不同的无锚定 MAIC 策略的偏倚、效率和置信区间覆盖。最后，我们提出了一种偏倚因子调整方法，当无锚定 MAIC 估计可能由于遗漏变量而存在偏倚时，用于估计真实效应。
重构数据在很大程度上代表了原始数据，即在一千次重复中，原始数据和重构数据之间的差异没有统计学意义。此外，随着协变量集的不完整性增加，无锚定 MAIC 估计的偏倚范围从最小到显著。此外，即使使用完整的协变量集，无锚定 MAIC 的置信区间估计也不理想。最后，我们提出的偏倚因子调整方法大大降低了遗漏变量的偏倚。
在分析生存数据时应谨慎使用无锚定 MAIC。偏倚因子可用于估计真实的治疗效果。
