Meta-analysis in evidence-based healthcare: a paradigm shift away from random effects is overdue
Meta-analysis in evidence-based healthcare: a paradigm shift away from random effects is overdue
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DOI：
10.1097/xeb.0000000000000125
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发表时间：
2017-12-01
影响因子：
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通讯作者：
Barendregt, Jan J.
中科院分区：
文献类型：
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作者：
Doi, Suhail A. R.;Furuya-Kanamori, Luis;Barendregt, Jan J.
每年有多达20000篇系统综述和荟萃分析发表，其结果影响医疗决策，因此荟萃分析方法的稳健性和可靠性成为世界上最重要的临床和公共卫生优先事项之一。证据合成使用固定效应或随机效应统计方法。固定效应方法已在很大程度上被随机效应方法所取代，因为研究效应的异质性导致误差估计较差。然而，尽管广泛使用和接受的随机效应方法来纠正这一点，它也仍然不令人满意，并继续遭受有缺陷的错误估计，构成严重威胁，以证据为基础的临床和公共卫生实践的决策。我们在这里讨论的问题与随机效应的方法，并证明存在更好的估计下的固定效应模型的框架，可以实现最佳的误差估计。我们主张紧急返回到早期的框架，更新解决这些问题，并得出结论，这样做可以显着提高元分析结果的可靠性，从而在医疗保健决策。
Each year up to 20 000 systematic reviews and meta-analyses are published whose results influence healthcare decisions, thus making the robustness and reliability of meta-analytic methods one of the world's top clinical and public health priorities. The evidence synthesis makes use of either fixed-effect or random-effects statistical methods. The fixed-effect method has largely been replaced by the random-effects method as heterogeneity of study effects led to poor error estimation. However, despite the widespread use and acceptance of the random-effects method to correct this, it too remains unsatisfactory and continues to suffer from defective error estimation, posing a serious threat to decision-making in evidence-based clinical and public health practice. We discuss here the problem with the random-effects approach and demonstrate that there exist better estimators under the fixed-effect model framework that can achieve optimal error estimation. We argue for an urgent return to the earlier framework with updates that address these problems and conclude that doing so can markedly improve the reliability of meta-analytical findings and thus decision-making in healthcare.