Department of Mathematics and Statistics, La Trobe University, Melbourne, Victoria 3086, Australia.
Stat Med. 2013 May 20;32(11):1842-64. doi: 10.1002/sim.5666. Epub 2012 Oct 23.
Standard meta-analytic theory assumes that study outcomes are normally distributed with known variances. However, methods derived from this theory are often applied to effect sizes having skewed distributions with estimated variances. Both shortcomings can be largely overcome by first applying a variance stabilizing transformation. Here we concentrate on study outcomes with Student t-distributions and show that we can better estimate parameters of fixed or random effects models with confidence intervals using stable weights or with profile approximate likelihood intervals following stabilization. We achieve even better coverage with a finite sample bias correction. Further, a simple t-interval provides very good coverage of an overall effect size without estimation of the inter-study variance. We illustrate the methodology on two meta-analytic studies from the medical literature, the effect of salt reduction on systolic blood pressure and the effect of opioids for the relief of breathlessness. Substantial simulation studies compare traditional methods with those newly proposed. We can apply the theoretical results to other study outcomes for which an effective variance stabilizer is available.
标准的荟萃分析理论假设研究结果呈正态分布，且方差已知。然而，该理论衍生的方法通常应用于具有偏态分布和估计方差的效应量。这两个缺点都可以通过先应用方差稳定转换来大大克服。在这里，我们集中研究具有学生 t 分布的研究结果，并表明我们可以通过稳定权重更好地估计固定或随机效应模型的参数，或者通过稳定化后的近似似然区间进行估计。我们通过有限样本偏差校正获得了更好的覆盖范围。此外，一个简单的 t 区间可以在不估计研究间方差的情况下，很好地覆盖整体效应量。我们在来自医学文献的两项荟萃分析研究中说明了该方法，一项是盐减少对收缩压的影响，另一项是阿片类药物缓解呼吸困难的效果。大量的模拟研究将传统方法与新提出的方法进行了比较。我们可以将理论结果应用于其他具有有效方差稳定器的研究结果。
