Heterogeneity Measure in Meta-analysis without Study-specific Variance Information

IF 0.8 Q2 MATHEMATICS Lobachevskii Journal of Mathematics Pub Date : 2024-05-14 DOI:10.1134/s1995080224600262
P. Sangnawakij, R. Sittimongkol
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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.

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没有特定研究方差信息的 Meta 分析中的异质性测量方法
摘要 在荟萃分析中,评估独立研究之间的异质性对于量化分散程度起着至关重要的作用。众所周知的 Higgins I2 统计量最常用于测量异质性。然而,该统计量所涉及的研究内方差问题会导致误读。另外,研究间变异系数(随机效应的标准差与效应之比)也很值得关注。目前这项工作的动力来自于只报告了样本平均值和样本大小的连续结果元分析数据。这些研究没有提供抽样方差估计值。在这种情况下,我们引入了基于轮廓似然法和引导法的均值差异估计器,并提出了变异系数估计器来测量均值差异的异质性。通过模拟确定了变异系数的统计能力。结果表明,通过最大轮廓似然估计得出的研究间变异系数比通过自举估计得出的变异系数偏差更小。利用德尔塔法进行方差估计得出的 Wald 型置信区间提供了合适的覆盖概率,且置信区间长度较短。
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CiteScore
1.50
自引率
42.90%
发文量
127
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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