Wang Zhenxun, Lin Lifeng, Murray Thomas, Hodges James S, Chu Haitao
Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA.
Department of Statistics, Florida State University, Tallahassee, FL 32306, USA.
Ann Appl Stat. 2021 Dec;15(4):1767-1787. doi: 10.1214/21-aoas1469. Epub 2021 Dec 21.
Network meta-analysis (NMA) is a powerful tool to compare multiple treatments directly and indirectly by combining and contrasting multiple independent clinical trials. Because many NMAs collect only a few eligible randomized controlled trials (RCTs), there is an urgent need to synthesize different sources of information, e.g., from both RCTs and single-arm trials. However, single-arm trials and RCTs may have different populations and quality, so that assuming they are exchangeable may be inappropriate. This article presents a novel method using a (CPV) to borrow variance (rather than mean) information from single-arm trials in an arm-based (AB) Bayesian NMA. We illustrate the advantages of this CPV method by reanalyzing an NMA of immune checkpoint inhibitors in cancer patients. Comprehensive simulations investigate the impact on statistical inference of including single-arm trials. The simulation results show that the CPV method provides efficient and robust estimation even when the two sources of information are moderately inconsistent.
网络荟萃分析（NMA）是一种通过合并和对比多个独立临床试验来直接和间接比较多种治疗方法的强大工具。由于许多网络荟萃分析仅收集了少数符合条件的随机对照试验（RCT），因此迫切需要综合不同来源的信息，例如来自随机对照试验和单臂试验的信息。然而，单臂试验和随机对照试验可能具有不同的人群和质量，因此假设它们可互换可能并不合适。本文提出了一种新颖的方法，即使用条件参数方差（CPV）在基于臂的（AB）贝叶斯网络荟萃分析中从单臂试验中借用方差（而非均值）信息。我们通过重新分析癌症患者免疫检查点抑制剂的网络荟萃分析来说明这种条件参数方差方法的优势。全面的模拟研究了纳入单臂试验对统计推断的影响。模拟结果表明，即使两个信息来源存在适度不一致，条件参数方差方法也能提供高效且稳健的估计。
