{"title":"Robust variance estimation in small meta-analysis with the standardized mean difference","authors":"Rrita Zejnullahi, Larry V. Hedges","doi":"10.1002/jrsm.1668","DOIUrl":null,"url":null,"abstract":"<p>Conventional random-effects models in meta-analysis rely on large sample approximations instead of exact small sample results. While random-effects methods produce efficient estimates and confidence intervals for the summary effect have correct coverage when the number of studies is sufficiently large, we demonstrate that conventional methods result in confidence intervals that are not wide enough when the number of studies is small, depending on the configuration of sample sizes across studies, the degree of true heterogeneity and number of studies. We introduce two alternative variance estimators with better small sample properties, investigate degrees of freedom adjustments for computing confidence intervals, and study their effectiveness via simulation studies.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"15 1","pages":"44-60"},"PeriodicalIF":5.0000,"publicationDate":"2023-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jrsm.1668","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research Synthesis Methods","FirstCategoryId":"99","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jrsm.1668","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Conventional random-effects models in meta-analysis rely on large sample approximations instead of exact small sample results. While random-effects methods produce efficient estimates and confidence intervals for the summary effect have correct coverage when the number of studies is sufficiently large, we demonstrate that conventional methods result in confidence intervals that are not wide enough when the number of studies is small, depending on the configuration of sample sizes across studies, the degree of true heterogeneity and number of studies. We introduce two alternative variance estimators with better small sample properties, investigate degrees of freedom adjustments for computing confidence intervals, and study their effectiveness via simulation studies.
期刊介绍:
Research Synthesis Methods is a reputable, peer-reviewed journal that focuses on the development and dissemination of methods for conducting systematic research synthesis. Our aim is to advance the knowledge and application of research synthesis methods across various disciplines.
Our journal provides a platform for the exchange of ideas and knowledge related to designing, conducting, analyzing, interpreting, reporting, and applying research synthesis. While research synthesis is commonly practiced in the health and social sciences, our journal also welcomes contributions from other fields to enrich the methodologies employed in research synthesis across scientific disciplines.
By bridging different disciplines, we aim to foster collaboration and cross-fertilization of ideas, ultimately enhancing the quality and effectiveness of research synthesis methods. Whether you are a researcher, practitioner, or stakeholder involved in research synthesis, our journal strives to offer valuable insights and practical guidance for your work.