Meta-Analysis for Medical Decisions (WP-19-05)


Charles F. Manski

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. Metaanalyses
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 meta-analysis. 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 decision- centered meta-analysis, the paper considers assessment of the effect
of anti-hypertensive drugs on blood pressure.

Charles F. Manski, Board of Trustees Professor in Economics and IPR Fellow, Northwestern University


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