匹配调整间接比较中的不确定性?比较方差估计方法的模拟研究。

IF 5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Conor O. Chandler, Irina Proskorovsky
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引用次数: 0

摘要

在卫生技术评估中,配对调整间接比较(MAIC)是最常用的配对比较方法,可控制各试验间基线特征的不平衡。MAIC 的主要挑战之一是需要适当考虑匹配过程带来的额外不确定性。有关 MAIC 方差估计的证据和指导有限。因此,我们进行了一项全面的蒙特卡罗模拟研究,以评估 108 种情况下不同统计方法的性能。我们比较了在二元和时间到事件结果的锚定和非锚定 MAIC 中进行方差估计的四种一般方法:(1) 使用原始权重的传统估计法 (CE);(2) 使用根据有效样本量(ESS)重新标定的权重的传统估计法;(3) 稳健的三明治估计法;以及 (4) 自举法。对三明治估计器和自举法的几种变体进行了测试。根据 95% 置信区间和变异率的经验覆盖概率对性能进行量化。当种群重叠程度较低或中等时,CE+原始权重低估了变异性。尽管存在一些理论上的限制,但 CE + ESS 权重在大多数情况下都能准确估计不确定性。三明治估计器的原始实施在 ESS 较小的情况下 MAIC 有向下的偏差,有限样本调整可明显改善。如果人口重合度较低且样本量有限,则 Bootstrapping 方法不稳定。在人群重叠度较高的情况下,所有方法都能得出有效的覆盖概率和标准误差。我们的研究结果表明,样本量、人群重叠度和结果类型是 MAICs 方差估计的重要考虑因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Uncertain about uncertainty in matching-adjusted indirect comparisons? A simulation study to compare methods for variance estimation

Uncertain about uncertainty in matching-adjusted indirect comparisons? A simulation study to compare methods for variance estimation

In health technology assessment, matching-adjusted indirect comparison (MAIC) is the most common method for pairwise comparisons that control for imbalances in baseline characteristics across trials. One of the primary challenges in MAIC is the need to properly account for the additional uncertainty introduced by the matching process. Limited evidence and guidance are available on variance estimation in MAICs. Therefore, we conducted a comprehensive Monte Carlo simulation study to evaluate the performance of different statistical methods across 108 scenarios. Four general approaches for variance estimation were compared in both anchored and unanchored MAICs of binary and time-to-event outcomes: (1) conventional estimators (CE) using raw weights; (2) CE using weights rescaled to the effective sample size (ESS); (3) robust sandwich estimators; and (4) bootstrapping. Several variants of sandwich estimators and bootstrap methods were tested. Performance was quantified on the basis of empirical coverage probabilities for 95% confidence intervals and variability ratios. Variability was underestimated by CE + raw weights when population overlap was poor or moderate. Despite several theoretical limitations, CE + ESS weights accurately estimated uncertainty across most scenarios. Original implementations of sandwich estimators had a downward bias in MAICs with a small ESS, and finite sample adjustments led to marked improvements. Bootstrapping was unstable if population overlap was poor and the sample size was limited. All methods produced valid coverage probabilities and standard errors in cases of strong population overlap. Our findings indicate that the sample size, population overlap, and outcome type are important considerations for variance estimation in MAICs.

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来源期刊
Research Synthesis Methods
Research Synthesis Methods MATHEMATICAL & COMPUTATIONAL BIOLOGYMULTID-MULTIDISCIPLINARY SCIENCES
CiteScore
16.90
自引率
3.10%
发文量
75
期刊介绍: Research Synthesis Methods is a reputable, peer-reviewed journal that focuses on the development and dissemination of methods for conducting systematic research synthesis. Our aim is to advance the knowledge and application of research synthesis methods across various disciplines. Our journal provides a platform for the exchange of ideas and knowledge related to designing, conducting, analyzing, interpreting, reporting, and applying research synthesis. While research synthesis is commonly practiced in the health and social sciences, our journal also welcomes contributions from other fields to enrich the methodologies employed in research synthesis across scientific disciplines. By bridging different disciplines, we aim to foster collaboration and cross-fertilization of ideas, ultimately enhancing the quality and effectiveness of research synthesis methods. Whether you are a researcher, practitioner, or stakeholder involved in research synthesis, our journal strives to offer valuable insights and practical guidance for your work.
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