Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany.
Faculty of Statistics, TU Dortmund University, Dortmund, Germany.
Res Synth Methods. 2020 Nov;11(6):913-919. doi: 10.1002/jrsm.1460. Epub 2020 Oct 21.
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).
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)。