Meta Analysis 2020:可怕的警报和解决方案

Jonathan J Shuster
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引用次数: 1

摘要

很难相信主流的荟萃分析存在重大缺陷,其主要目标是从一组已完成的研究中提供效应大小的汇总估计。然而,由于主流将权重和/或样本量视为常数,而不是明确的严重随机变量,因此情况正是如此。此外,主流随机效应模型不允许权重和效应大小之间的关联,如果错误,可能会导致重大偏差。对于以下情况,我们提供了一个修复方案,依靠聚类抽样的比率估计,为以下情况产生简单有效的渐近方法:平均值或比例的估计,随机试验的平均值或比率的差异,随机试验相对风险的估计,以及Bland Altman研究的旨在用非侵入性措施取代侵入性措施的重复措施。主流方法的一个恐怖故事是,当我们保持研究点估计不变,但普遍将研究标准误差降低了30%时,一项重大研究中的高度显著结果变得微不足道。2019年每月发表1400多篇荟萃分析论文,将这篇论文作为缓解这种情况的跳板至关重要。
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Meta-Analysis 2020: A Dire Alert and a Fix
It is hard to believe that mainstream meta-analysis, whose primary objective is to provide a summary estimate of effect size from a set of completed studies, has a major flaw. Yet because the mainstream treats weights and/or sample sizes as constants, rather than unequivocally seriously random variables, this is exactly the situation. Further, the mainstream random effects model does not permit association between weights and effect size, which if false, can lead to major bias. For the following scenarios, we provide a fix, relying on ratio estimation from cluster sampling, to produce simple and valid asymptotic methods for the following scenarios: Estimation of means or proportions, differences of means or proportion from randomized trials, estimation of relative risk from randomized trials, and repeated measures Bland-Altman studies aimed at replacing invasive by non-invasive measures. One horror story for mainstream methods saw a highly significant result in a major study become insignificant when we kept the study point estimates the same but universally cut the study standard errors by 30%. With over 1400 meta-analyses papers published per month in 2019, it is essential to use this paper as a springboard to mitigate this situation.
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