{"title":"Heterogeneity Measure in Meta-analysis without Study-specific Variance Information","authors":"P. Sangnawakij, R. Sittimongkol","doi":"10.1134/s1995080224600262","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Assessing heterogeneity between the independent studies in a meta-analysis plays a critical role in quantifying the amount of dispersion. The well-known Higgins’ I2 statistic has been used most often for measuring heterogeneity. However, the problem of the within-study variances involved in this measure is discussed, which leads to misinterpretation. Alternatively, the between-study coefficient of variation, the ratio of the standard deviation of the random effects to the effect, is of interest. This current work is motivated by meta-analytic data on continuous outcomes reported only the sample means and sample sizes. No sampling variance estimate is available in the studies. In such a case, we introduce the mean difference estimator based on the profile likelihood and bootstrap methods and propose the coefficient of variation estimator for measuring the heterogeneity of the mean differences. The statistical power of the coefficient of variation is determined based on simulations. The results indicate that the estimated between-study coefficient of variation derived from maximum profile likelihood estimation has a lower bias than that obtained from bootstrap estimation. The Wald-type confidence interval using variance estimation derived from the delta method provides a suitable coverage probability and has a short length interval.</p>","PeriodicalId":46135,"journal":{"name":"Lobachevskii Journal of Mathematics","volume":null,"pages":null},"PeriodicalIF":0.8000,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Lobachevskii Journal of Mathematics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1134/s1995080224600262","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS","Score":null,"Total":0}
引用次数: 0
Abstract
Assessing heterogeneity between the independent studies in a meta-analysis plays a critical role in quantifying the amount of dispersion. The well-known Higgins’ I2 statistic has been used most often for measuring heterogeneity. However, the problem of the within-study variances involved in this measure is discussed, which leads to misinterpretation. Alternatively, the between-study coefficient of variation, the ratio of the standard deviation of the random effects to the effect, is of interest. This current work is motivated by meta-analytic data on continuous outcomes reported only the sample means and sample sizes. No sampling variance estimate is available in the studies. In such a case, we introduce the mean difference estimator based on the profile likelihood and bootstrap methods and propose the coefficient of variation estimator for measuring the heterogeneity of the mean differences. The statistical power of the coefficient of variation is determined based on simulations. The results indicate that the estimated between-study coefficient of variation derived from maximum profile likelihood estimation has a lower bias than that obtained from bootstrap estimation. The Wald-type confidence interval using variance estimation derived from the delta method provides a suitable coverage probability and has a short length interval.
期刊介绍：
Lobachevskii Journal of Mathematics is an international peer reviewed journal published in collaboration with the Russian Academy of Sciences and Kazan Federal University. The journal covers mathematical topics associated with the name of famous Russian mathematician Nikolai Lobachevsky (Lobachevskii). The journal publishes research articles on geometry and topology, algebra, complex analysis, functional analysis, differential equations and mathematical physics, probability theory and stochastic processes, computational mathematics, mathematical modeling, numerical methods and program complexes, computer science, optimal control, and theory of algorithms as well as applied mathematics. The journal welcomes manuscripts from all countries in the English language.
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