Meta-Generalized Method of Moments for Integrating External Studies under Heterogeneity

Thomas M. Menino Convention & Exhibition Center 

Integrating external studies can improve precision and statistical efficiency beyond using an internal study alone, yet heterogeneity across external studies can distort internal estimates. Existing methods may require individual-level external data or strong distributional assumptions and may yield invalid inference under between-study heterogeneity. We propose mGMM, a semiparametric framework that combines generalized method of moments with a random-effects model to borrow external summary information while accounting for heterogeneity. Unlike methods that require external studies to estimate the same target parameter, mGMM uses auxiliary external information to improve inference for the internal-study target. Simulations show consistent estimation, valid inference, and efficiency gains over internal-only analyses under heterogeneity. Applied to colorectal cancer tumor sequencing data while integrating external smoking summaries for established molecular subtypes, mGMM identifies smoking associations that vary by mutation-defined subtype and were not possible using the internal data alone, providing etiologic insight beyond internal-only analyses.

Data integration

External summary information

Generalized method of moments

Random-effects model

Study heterogeneity

Statistical efficiency 

Section on Statistics in Epidemiology