Branscum Adam J, Hanson Timothy E
Departments of Biostatistics, Statistics, and Epidemiology, University of Kentucky, Lexington, Kentucky 40536, U.S.A. email:
Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota 55455, U.S.A. email:
Biometrics. 2008 Sep;64(3):825-833. doi: 10.1111/j.1541-0420.2007.00946.x. Epub 2007 Dec 6.
Summary. A common goal in meta-analysis is estimation of a single effect measure using data from several studies that are each designed to address the same scientific inquiry. Because studies are typically conducted in geographically disperse locations, recent developments in the statistical analysis of meta-analytic data involve the use of random effects models that account for study-to-study variability attributable to differences in environments, demographics, genetics, and other sources that lead to heterogeneity in populations. Stemming from asymptotic theory, study-specific summary statistics are modeled according to normal distributions with means representing latent true effect measures. A parametric approach subsequently models these latent measures using a normal distribution, which is strictly a convenient modeling assumption absent of theoretical justification. To eliminate the influence of overly restrictive parametric models on inferences, we consider a broader class of random effects distributions. We develop a novel hierarchical Bayesian nonparametric Polya tree mixture (PTM) model. We present methodology for testing the PTM versus a normal random effects model. These methods provide researchers a straightforward approach for conducting a sensitivity analysis of the normality assumption for random effects. An application involving meta-analysis of epidemiologic studies designed to characterize the association between alcohol consumption and breast cancer is presented, which together with results from simulated data highlight the performance of PTMs in the presence of nonnormality of effect measures in the source population.
摘要。荟萃分析的一个常见目标是使用来自多项研究的数据来估计单一效应量，这些研究均旨在解决相同的科学问题。由于研究通常在地理位置分散的地方进行，荟萃分析数据统计分析的最新进展涉及使用随机效应模型，该模型考虑了因环境、人口统计学、遗传学以及导致人群异质性的其他来源的差异而产生的研究间变异性。基于渐近理论，根据正态分布对特定研究的汇总统计量进行建模，其均值代表潜在的真实效应量。随后，一种参数方法使用正态分布对这些潜在量进行建模，严格来说，这只是一个方便的建模假设，缺乏理论依据。为了消除过度限制的参数模型对推断的影响，我们考虑更广泛的一类随机效应分布。我们开发了一种新颖的分层贝叶斯非参数波利亚树混合（PTM）模型。我们提出了用于检验PTM与正态随机效应模型的方法。这些方法为研究人员提供了一种直接的方法，用于对随机效应的正态性假设进行敏感性分析。本文展示了一个涉及旨在描述饮酒与乳腺癌之间关联的流行病学研究荟萃分析的应用，该应用与模拟数据的结果一起突出了PTM在源人群效应量非正态情况下的性能。
