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零细胞校正在随机效应荟萃分析中的应用。

Zero-cell corrections in random-effects meta-analyses.

机构信息

Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany.

Faculty of Statistics, TU Dortmund University, Dortmund, Germany.

出版信息

Res Synth Methods. 2020 Nov;11(6):913-919. doi: 10.1002/jrsm.1460. Epub 2020 Oct 21.

Abstract

The standard estimator for the log odds ratio (the unconditional maximum likelihood estimator) and the delta-method estimator for its standard error are not defined if the corresponding 2 × 2 table contains at least one "zero cell". This is also an issue when estimating the overall log odds ratio in a meta-analysis. It is well known that correcting for zero cells by adding a small increment should be avoided. Nevertheless, these zero-cell corrections continue to be used. With this Brief Method Note, we want to warn of a particularly bad zero-cell correction. For this, we conduct a simulation study comparing the following two zero-cell corrections under the ordinary random-effects model: (a) adding to all cells of all the individual studies' 2 × 2 tables independently of any zero-cell occurrences and (b) adding to all cells of only those 2 × 2 tables containing at least one zero cell. The main finding is that correction (a) performs worse than correction (b). Thus, we strongly discourage the use of correction (a).

摘要

如果相应的 2×2 表中至少包含一个“零单元格”,则无法定义对数优势比的标准估计量(无条件最大似然估计量)和其标准误差的德尔塔方法估计量。在荟萃分析中估计总体对数优势比时也会出现此问题。众所周知,应避免通过添加小增量来纠正零单元格。尽管如此,这些零单元格校正仍在继续使用。在这份简要方法说明中,我们想警告一种特别糟糕的零单元格校正。为此,我们在普通随机效应模型下进行了一项模拟研究,比较了以下两种零单元格校正方法:(a) 将 独立添加到所有个体研究的 2×2 表的所有单元格中,无论是否存在零单元格;(b) 将 仅添加到包含至少一个零单元格的所有 2×2 表的所有单元格中。主要发现是校正 (a) 的性能比校正 (b) 差。因此,我们强烈反对使用校正 (a)。

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