Regression modeling of combined data from multiple sample surveys

Combined data from multiple sample surveys are often used in population-based epidemiologic studies. Combining data can be beneficial in that sampling errors are reduced and coverage biases are corrected. Also, it is often necessary in order to use information lacking in one survey but available in another. We propose an estimation equations method for generalized linear models from the combined data. The estimation procedures for logistic regression models and Poisson regression models are developed. An example of estimating the relative risk of death by smoking status is used as an illustration and a simulation study is performed to examine the performance of the method.