From the Department of Economics, Institute for Policy Research Northwestern University, Evanston, IL.
Epidemiology. 2020 May;31(3):345-352. doi: 10.1097/EDE.0000000000001178.
Meta-analysis is widely used to combine the findings of multiple disparate studies of health risks or treatment response. Meta-analysis often uses a random-effects model to express heterogeneity across studies. The model interprets a weighted average of study-specific estimates as an estimate of a mean parameter across a hypothetical population of studies. The relevance of this methodology to patient care is not evident. Clinicians need to assess risks and choose treatments for populations of patients, not for populations of studies. This article draws on econometric research on partial identification to propose principles for patient-centered meta-analysis. One specifies a patient prediction of concern and determines what each available study reveals. Given common imperfections in internal and external validity, studies typically yield credible set-valued rather than point predictions. Thus, a study may enable one to conclude that a probability of disease, or mean treatment response, lies within a range of possibilities. Patient-centered meta-analysis would combine the findings of multiple studies by computing the intersection of the set-valued predictions that they yield.
荟萃分析被广泛用于整合多个健康风险或治疗反应的不同研究结果。荟萃分析通常使用随机效应模型来表达研究间的异质性。该模型将研究特异性估计值的加权平均值解释为假设研究总体中一个均值参数的估计值。这种方法对于患者护理的相关性并不明显。临床医生需要评估风险并为患者群体选择治疗方法，而不是为研究群体选择。本文借鉴计量经济学中关于部分识别的研究，提出了以患者为中心的荟萃分析原则。一种方法是指定一个关注的患者预测，并确定每个可用研究揭示的内容。鉴于内部和外部有效性的常见缺陷，研究通常会产生可信的集值预测，而不是点预测。因此，一项研究可以使人们得出结论，即疾病的概率或平均治疗反应在多种可能性范围内。以患者为中心的荟萃分析将通过计算它们产生的集值预测的交集来合并多个研究的结果。
