Meta-Analysis for Medical Decisions
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 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.
I am grateful to Michael Gmeiner for research assistance and comments. The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research.