Toward Credible Patient-centered Meta-analysis

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.