Division of Cancer Epidemiology and Genetics, National Cancer Institute, 6120 Executive Blvd., EPS 8047, Rockville, MD 20892, USA.
Biostatistics. 2013 Apr;14(2):273-83. doi: 10.1093/biostatistics/kxs035. Epub 2012 Sep 21.
Individual patient-data meta-analysis of randomized controlled trials is the gold standard for investigating how patient factors modify the effectiveness of treatment. Because participant data from primary studies might not be available, reliable alternatives using published data are needed. In this paper, I show that the maximum likelihood estimates of a participant-level linear random effects meta-analysis with a patient covariate-treatment interaction can be determined exactly from aggregate data when the model's variance components are known. I provide an equivalent aggregate-data EM algorithm and supporting software with the R package ipdmeta for the estimation of the "interaction meta-analysis" when the variance components are unknown. The properties of the methodology are assessed with simulation studies. The usefulness of the methods is illustrated with analyses of the effect modification of cholesterol and age on pravastatin in the multicenter placebo-controlled regression growth evaluation statin study. When a participant-level meta-analysis cannot be performed, aggregate-data interaction meta-analysis is a useful alternative for exploring individual-level sources of treatment effect heterogeneity.
对随机对照试验的个体患者数据进行荟萃分析是研究患者因素如何改变治疗效果的金标准。由于主要研究的参与者数据可能不可用，因此需要使用已发表的数据来获得可靠的替代方法。在本文中，我表明，当模型的方差分量已知时，可以从汇总数据中准确确定具有患者协变量-治疗相互作用的参与者水平线性随机效应荟萃分析的最大似然估计。我为未知方差分量的情况下提供了等效的汇总数据 EM 算法和支持软件，以及 R 包 ipdmeta 中的“交互荟萃分析”估计。使用模拟研究评估了该方法的性质。通过对多中心安慰剂对照回归增长评估他汀类研究中胆固醇和年龄对普伐他汀的作用修饰的分析说明了该方法的实用性。当无法进行个体水平荟萃分析时，汇总数据交互荟萃分析是探索治疗效果异质性个体水平来源的有用替代方法。
