Meta-Analysis With Fixed, Unknown, Study-Specific Parameters

• 成果类型: Article
• 署名作者: Claggett, Brian; Xie, Minge; Tian, Lu
• 署名单位: Harvard University; Harvard Medical School; Rutgers University System; Rutgers University New Brunswick; Stanford University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2014.957288
• 发表日期: 2014
• 页码: 1660-1671
• 关键词: frequentist distribution estimator; EMPIRICAL BAYES; confidence distribution; likelihood; intervals

摘要

Meta-analysis is a valuable tool for combining information from independent studies. However, most common meta-analysis techniques rely on distributional assumptions that are difficult, if not impossible, to verify. For instance, in the commonly used fixed-effects and random-effects models, we take for granted that the underlying study-level parameters are either exactly the same across individual studies or that they are realizations of a random sample from a population, often under a parametric distributional assumption. In this article, we present a new framework for summarizing information obtained from multiple studies and make inference that is not dependent on any distributional assumption for the study-level parameters. Specifically, we assume the study-level parameters are unknown, fixed parameters and draw inferences about, for example, the quantiles of this set of parameters using study-specific summary statistics. This type of problem is known to be quite challenging (see Hall and Miller). We use a novel resampling method via the confidence distributions of the study-level parameters to construct confidence intervals for the above quantiles. We justify the validity of the interval estimation procedure asymptotically and compare the new procedure with the standard bootstrapping method. We also illustrate our proposal with the data from a recent meta-analysis of the treatment effect from an antioxidant on the prevention of contrast-induced nephropathy.