Meta-Analysis for Medical Decisions

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 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.