Combining Statistical Evidence

被引:16
作者
Kulinskaya, Elena [1 ]
Morgenthaler, Stephan [2 ]
Staudte, Robert G. [3 ]
机构
[1] Univ E Anglia, Sch Comp Sci, Norwich NR4 7TJ, Norfolk, England
[2] Ecole Polytech Fed Lausanne, CH-1015 Lausanne, Switzerland
[3] La Trobe Univ, Dept Math & Stat, Melbourne, Vic 3086, Australia
基金
瑞士国家科学基金会;
关键词
Review; meta-analysis; effect size; random effects; meta-regression; software; RANDOM-EFFECTS METAANALYSIS; ROBUST VARIANCE-ESTIMATION; TRIAL SEQUENTIAL-ANALYSIS; INDIVIDUAL PATIENT DATA; RANDOM-EFFECTS MODEL; ISPOR TASK-FORCE; ONE-WAY ANOVA; CUMULATIVE METAANALYSIS; PUBLICATION BIAS; MULTIVARIATE METAANALYSIS;
D O I
10.1111/insr.12037
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
The combination of evidence from independent studies has a curious history. The origins reach back at least to the beginning of the 20th century. Since the mid-1970s, meta-analysis has become popular in several fields, among them medical statistics and the behavioural sciences. The most widely used procedures were perfected in early papers, and subsequently, a kind of groupthink has taken hold of meta-analysis. This explains the need for a review in a statistics journal, destined for a statistical audience. Meta-analysis is not a hot research topic among graduate students in statistics, and by writing this article, we hope to change this. We wish to point out the shortcomings of the mainstream view and exhibit some of the open problems that await the attention of statistical researchers. A host of competent reviews of meta-analysis have been published, and several book-length treatments are also available. We have listed many of these in the bibliography but cannot guarantee completeness.
引用
收藏
页码:214 / 242
页数:29
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