Rosenberger Kristine J, Xing Aiwen, Murad Mohammad Hassan, Chu Haitao, Lin Lifeng
Department of Statistics, Florida State University, Tallahassee, FL, 32306, USA.
Evidence-Based Practice Center, Mayo Clinic, Rochester, MN, USA.
J Gen Intern Med. 2021 Apr;36(4):1049-1057. doi: 10.1007/s11606-020-06357-1. Epub 2021 Jan 5.
Network meta-analysis (NMA) is a popular tool to compare multiple treatments in medical research. It is frequently implemented via Bayesian methods. The prior choice of between-study heterogeneity is critical in Bayesian NMAs. This study evaluates the impact of different priors for heterogeneity on NMA results.
We identified all NMAs with binary outcomes published in The BMJ, JAMA, and The Lancet during 2010-2018, and extracted information about their prior choices for heterogeneity. Our primary analyses focused on those with publicly available full data. We re-analyzed the NMAs using 3 commonly-used non-informative priors and empirical informative log-normal priors. We obtained the posterior median odds ratios and 95% credible intervals of all comparisons, assessed the correlation among different priors, and used Bland-Altman plots to evaluate their agreement. The kappa statistic was also used to evaluate the agreement among these priors regarding statistical significance.
Among the selected Bayesian NMAs, 52.3% did not specify the prior choice for heterogeneity, and 84.1% did not provide rationales. We re-analyzed 19 NMAs with full data available, involving 894 studies, 173 treatments, and 395,429 patients. The correlation among posterior median (log) odds ratios using different priors were generally very strong for NMAs with over 20 studies. The informative priors produced substantially narrower credible intervals than non-informative priors, especially for NMAs with few studies. Bland-Altman plots and kappa statistics indicated strong overall agreement, but this was not always the case for a specific NMA.
Priors should be routinely reported in Bayesian NMAs. Sensitivity analyses are recommended to examine the impact of priors, especially for NMAs with relatively small sample sizes. Informative priors may produce substantially narrower credible intervals for such NMAs.
网络荟萃分析（NMA）是医学研究中比较多种治疗方法的常用工具。它通常通过贝叶斯方法实施。研究间异质性的先验选择在贝叶斯NMA中至关重要。本研究评估了不同异质性先验对NMA结果的影响。
我们识别了2010年至2018年期间发表在《英国医学杂志》《美国医学会杂志》和《柳叶刀》上的所有具有二元结局的NMA，并提取了它们关于异质性先验选择的信息。我们的主要分析集中在那些有公开可用完整数据的研究上。我们使用3种常用的非信息性先验和经验性信息对数正态先验对这些NMA进行了重新分析。我们获得了所有比较的后验中位数优势比和95%可信区间，评估了不同先验之间的相关性，并使用布兰德-奥特曼图来评估它们的一致性。kappa统计量也用于评估这些先验在统计显著性方面的一致性。
在选定的贝叶斯NMA中，52.3%未指定异质性的先验选择，84.1%未提供理由。我们对19项有完整数据的NMA进行了重新分析，涉及894项研究、173种治疗方法和395429名患者。对于有超过20项研究的NMA，使用不同先验的后验中位数（对数）优势比之间的相关性通常非常强。信息性先验产生的可信区间比非信息性先验窄得多，尤其是对于研究较少的NMA。布兰德-奥特曼图和kappa统计量表明总体一致性很强，但对于特定的NMA并非总是如此。
在贝叶斯NMA中应常规报告先验。建议进行敏感性分析以检查先验的影响，特别是对于样本量相对较小的NMA。信息性先验可能会为这类NMA产生明显更窄的可信区间。
