Unreported standard errors in meta-analysis

Q4 Mathematics Statistics in Transition Pub Date : 2021-12-01 DOI:10.21307/stattrans-2021-035
N. Longford
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引用次数: 0

Abstract

Abstract A study that would otherwise be eligible is commonly excluded from a meta-analysis when the standard error of its treatment-effect estimator, or the estimate of the variance of the outcomes, is not reported and cannot be recovered from the available information. This is wasteful when the estimate of the treatment effect is reported. We assess the loss of information caused by this practice and explore methods of imputation for the missing variance. The methods are illustrated on two sets of examples, one constructed specifically for illustration and another based on a published systematic review.
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荟萃分析中未报告的标准误差
当治疗效果估计值的标准误差或结果方差估计值未报告且无法从现有信息中恢复时,本应符合条件的研究通常会被排除在荟萃分析之外。当报告治疗效果的估计时,这是一种浪费。我们评估了这种做法造成的信息损失,并探讨了缺失方差的归算方法。这些方法在两组例子上进行了说明,一组是专门为说明而构建的,另一组是基于已发表的系统综述。
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来源期刊
Statistics in Transition
Statistics in Transition Decision Sciences-Statistics, Probability and Uncertainty
CiteScore
1.00
自引率
0.00%
发文量
0
审稿时长
9 weeks
期刊介绍: Statistics in Transition (SiT) is an international journal published jointly by the Polish Statistical Association (PTS) and the Central Statistical Office of Poland (CSO/GUS), which sponsors this publication. Launched in 1993, it was issued twice a year until 2006; since then it appears - under a slightly changed title, Statistics in Transition new series - three times a year; and after 2013 as a regular quarterly journal." The journal provides a forum for exchange of ideas and experience amongst members of international community of statisticians, data producers and users, including researchers, teachers, policy makers and the general public. Its initially dominating focus on statistical issues pertinent to transition from centrally planned to a market-oriented economy has gradually been extended to embracing statistical problems related to development and modernization of the system of public (official) statistics, in general.
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