Year: 2019
Type: report
Abstract: Statisticians have proposed meta-analysis to combine the findings of multiple studies of health risks or treatment response.The standard practice is to compute a weighted-average of the estimates.Yet it is not clear how to interpret a weighted average of estimates reported in disparate studies.Meta-analyses often answer this question through the lens of a random-effects model, which interprets a weighted average of estimates as an estimate of a mean parameter across a hypothetical population of studies.The relevance to medical decision making is obscure.Decision-centered research should aim to inform risk assessment and treatment for populations of patients, not populations of studies.This paper lays out principles for decision-centered metaanalysis.One first specifies a prediction of interest and next examines what each available study credibly reveals.Such analysis typically yields a set-valued prediction rather than a point prediction.Thus, one uses each study to conclude that a probability of disease, or mean treatment response, lies within a range of possibilities.Finally, one combines the available studies by computing the intersection of the set-valued predictions that they yield.To demonstrate decisioncentered meta-analysis, the paper considers assessment of the effect of anti-hypertensive drugs on blood pressure.
DOI: 10.3386/w25504
Author: Charles Manski
Institutions: Northwestern University, National Bureau of Economic Research
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2912258609