© 1995 by Biometrika Trust
Aggregate data studies of disease risk factors
1Division of Public Health Sciences, Fred Hutchinson Cancer Research Center Seattle, Washington 98104, U.S.A.
2Department of Biostatistics SC-32, University of Washington Seattle, Washington 98195, U.S.A.
Statistical methods are proposed for estimating relative rate parameters, based on estimated disease rates and covariate data from random samples of individuals from each of several cohorts. A random effects model is used to derive mean and variance models for estimated disease rates. Estimating equations for relative rate parameters are then developed by replacing cohort covariate averages by corresponding sample averages. The asymptotic distribution of regression parameter estimates is derived, and the asymptotic bias is shown to be small, even if covariates are contaminated by classical random measurement errors, provided the covariate sample size in each cohort is not small. Simulation studies, motivated by international data on diet and breast cancer, provide insights into the properties of the proposed estimators.
Key Words: Aggregate data • Confounding • Covariate measurement error • Ecological studies • Estimating equation • Finite population sampling • Multiple populations • Random effects • Relative risk • Sample survey
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