York Trials Unit, Department of Health Sciences, University of York, York YO10 5DD, UK.
York Trials Unit, Department of Health Sciences, University of York, York YO10 5DD, UK.
J Clin Epidemiol. 2018 Mar;95:55-62. doi: 10.1016/j.jclinepi.2017.10.001. Epub 2017 Oct 13.
To perform a worked example of an approach that can be used to identify and remove potentially biased trials from meta-analyses via the analysis of baseline variables.
True randomisation produces treatment groups that differ only by chance; therefore, a meta-analysis of a baseline measurement should produce no overall difference and zero heterogeneity. A meta-analysis from the British Medical Journal, known to contain significant heterogeneity and imbalance in baseline age, was chosen. Meta-analyses of baseline variables were performed and trials systematically removed, starting with those with the largest t-statistic, until the I measure of heterogeneity became 0%, then the outcome meta-analysis repeated with only the remaining trials as a sensitivity check.
We argue that heterogeneity in a meta-analysis of baseline variables should not exist, and therefore removing trials which contribute to heterogeneity from a meta-analysis will produce a more valid result. In our example none of the overall outcomes changed when studies contributing to heterogeneity were removed. We recommend routine use of this technique, using age and a second baseline variable predictive of outcome for the particular study chosen, to help eliminate potential bias in meta-analyses.
通过分析基线变量，提供一种可用于从荟萃分析中识别和剔除潜在偏倚试验的方法示例。
真正的随机化产生的治疗组仅因偶然因素而有所不同；因此，对基线测量值的荟萃分析不应产生总体差异和零异质性。选择了已知在基线年龄方面存在显著异质性和不平衡的英国医学杂志上的一篇荟萃分析。进行了基线变量的荟萃分析，并系统地剔除试验，从具有最大 t 统计量的试验开始，直到 I 测量的异质性变为 0%，然后仅用剩余的试验重复进行结果荟萃分析，作为敏感性检查。
我们认为，基线变量荟萃分析中的异质性不应该存在，因此从荟萃分析中剔除导致异质性的试验将产生更有效的结果。在我们的例子中，当剔除导致异质性的研究时，所有总体结果都没有改变。我们建议常规使用该技术，使用年龄和第二个预测特定研究结果的基线变量，以帮助消除荟萃分析中的潜在偏差。
