Aggregate Data Studies of Disease Risk Factors
提出基于多个队列的估计疾病率和协变量数据来估计相对率参数的统计方法，使用随机效应模型和估计方程，并推导了渐近分布，模拟研究显示该方法在协变量存在测量误差时仍有效。
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.