Rhodes Kirsty M, Turner Rebecca M, Higgins Julian P T
MRC Biostatistics Unit, Institute of Public Health, Cambridge, UK.
School of Social and Community Medicine, University of Bristol, UK.
Res Synth Methods. 2016 Dec;7(4):346-370. doi: 10.1002/jrsm.1193. Epub 2015 Dec 17.
This paper investigates how inconsistency (as measured by the I statistic) among studies in a meta-analysis may differ, according to the type of outcome data and effect measure. We used hierarchical models to analyse data from 3873 binary, 5132 continuous and 880 mixed outcome meta-analyses within the Cochrane Database of Systematic Reviews. Predictive distributions for inconsistency expected in future meta-analyses were obtained, which can inform priors for between-study variance. Inconsistency estimates were highest on average for binary outcome meta-analyses of risk differences and continuous outcome meta-analyses. For a planned binary outcome meta-analysis in a general research setting, the predictive distribution for inconsistency among log odds ratios had median 22% and 95% CI: 12% to 39%. For a continuous outcome meta-analysis, the predictive distribution for inconsistency among standardized mean differences had median 40% and 95% CI: 15% to 73%. Levels of inconsistency were similar for binary data measured by log odds ratios and log relative risks. Fitted distributions for inconsistency expected in continuous outcome meta-analyses using mean differences were almost identical to those using standardized mean differences. The empirical evidence on inconsistency gives guidance on which outcome measures are most likely to be consistent in particular circumstances and facilitates Bayesian meta-analysis with an informative prior for heterogeneity. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.
本文研究了在荟萃分析中，研究间的不一致性（以I统计量衡量）如何根据结局数据类型和效应量而有所不同。我们使用分层模型分析了Cochrane系统评价数据库中3873项二分类、5132项连续性和880项混合结局的荟萃分析数据。获得了未来荟萃分析中预期的不一致性预测分布，这可为研究间方差的先验提供参考。风险差的二分类结局荟萃分析和连续性结局荟萃分析的不一致性估计平均最高。对于一般研究环境中计划进行的二分类结局荟萃分析，对数比值比之间不一致性的预测分布中位数为22%，95%置信区间为12%至39%。对于连续性结局荟萃分析，标准化均数差之间不一致性的预测分布中位数为40%，95%置信区间为15%至73%。通过对数比值比和对数相对风险测量的二分类数据的不一致性水平相似。使用均数差的连续性结局荟萃分析中预期的不一致性拟合分布与使用标准化均数差的几乎相同。关于不一致性的实证证据为哪些结局指标在特定情况下最可能一致提供了指导，并有助于使用异质性信息先验的贝叶斯荟萃分析。© 2015作者。《研究综合方法》由John Wiley & Sons, Ltd出版。© 2015作者。《研究综合方法》由John Wiley & Sons, Ltd出版。
