A method for the meta-analysis of mutually exclusive binary outcomes
A method for the meta-analysis of mutually exclusive binary outcomes
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DOI：
10.1002/sim.3299
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发表时间：
2008-09-20
影响因子：
2
通讯作者：
Olkin, Ingram
中科院分区：
文献类型：
--
作者：
Trikalinos, Thomas A.;Olkin, Ingram
多结果的荟萃分析需要考虑不同结果之间的研究内相关性。在这里，我们专注于二元结果的荟萃分析，这是相互排斥和详尽的。互斥结果的效应量之间的相关性为负，可从现有数据中获得。我们提出了固定效应和随机效应方法，这些方法解释了负相关性，并为边际结果特定效应大小和结果之间的相对效应大小提供了正确的同时置信区间。给出了比值比、风险比、风险差和arcsin风险差的计算公式。本文提出了一项荟萃分析的例子，该荟萃分析是针对早期乳腺癌的随机试验，放疗和乳房切除术合并腋窝淋巴结清除与仅乳房切除术合并腋窝淋巴结清除。在单独的荟萃分析中检查了乳腺癌死亡和继发于其他原因的死亡的互斥结果，并考虑了结果之间的相关性。我们认为二元数据的元分析中互斥的结果在多项设置中得到最佳分析。当荟萃分析只检查几个相互排斥的结果中的一个时，这也可能适用。对于大样本量和/或低事件数，结果特定效应大小之间的协方差很小，忽略它们或考虑它们将导致任何实际目的的类似估计。然而。当相互排斥的结果被评估时，元分析者应该探索单个元分析结果的稳健性。版权所有(c) 2008约翰威利父子有限公司
Meta-analyses of multiple outcomes need to take into account the within-study correlation across the different outcomes. Here we focus on the meta-analysis of dichotomous outcomes that are Mutually exclusive and exhaustive. Correlations between effect sizes for mutually exclusive outcomes are negative and can be obtained from data already available. We present both fixed-effects and random-effects methods that account for the negative correlations and yield correct Simultaneous confidence intervals for both the marginal outcome-specific effect sizes and the relative effect sizes between outcomes. Formulae for the odds ratio, risk ratio, risk difference, and the differences in the arcsin-transformed risks are provided. An example of a meta-analysis of randomized trials of radiotherapy and mastectomy with axillary lymph node clearance versus only mastectomy with axillary clearance for early breast cancer is presented. The Mutually exclusive outcomes of breast cancer deaths and deaths secondary to other causes are examined in separate meta-analyses, and also by taking the between-outcome correlation into account. We argue that mutually exclusive outcomes in the meta-analyses of binary data are optimally analyzed in a multinomial setting. This may also be applicable when a meta-analysis examines only one out of several mutually exclusive outcomes. For large sample sizes and/or low event counts, the covariances between outcome-specific effect sizes are small, and either ignoring them or accounting for them Would result in similar estimates for any practical purpose. However. meta-analysts should explore the robustness of the findings from individual meta-analyses when mutually exclusive outcomes are assessed. Copyright (c) 2008 John Wiley & Sons, Ltd.