An integrated analysis of individual and aggregated health data using estimating equations.

Analyses of individual disease-exposure data within a population are useful when exposure of interest varies sufficiently within the population. When the within-population variance of exposure is limited, however, power of the individual-data analysis is reduced. In such situations, aggregated-data analyses of disease data across populations, with a sample of individual exposure data from each population, can be powerful in estimating the exposure effect if between population variation of exposure is large. In this paper, we consider a new analytical framework that is a combination of the individual- and aggregated-data analyses, based on an estimating equation approach. The proposed analysis utilizes strengths from individual data and aggregated data in the estimation of the exposure effect of interest, depending on which of the exposure variations (within- versus between-population) dominates. Simulation studies under various different scenarios were performed to show the strengths of the proposed approach in the estimation of the exposure effects of interest.