Advances in Methods for Integrating Heterogeneous Health Data

Thomas M. Menino Convention & Exhibition Center 

Meta-analysis typically combines measures of association across multiple studies. Outside of carefully-controlled lab experiments, these are not plausibly identical and the underlying effects will differ. Methods for describing this heterogeneity are limited: the default choices are either purely statistical measures of the differences between the estimates (I-squared or Cochran's Q) or - making particular modeling assumptions - the variance of an assumed random effects distribution, typically denoted tau-squared. We present a simple alternative measure of heterogeneity of the underlying parameters, measured on the same scale as them, and straightforward plug-in estimates of it. As well as providing inference on this measure of spread, we show how interpretably-penalized versions of it lead to simple sparse descriptions of the set of heterogeneous parameters. Several examples will be given.

meta-analysis

heterogeneity

Bayesian