title: Regression modeling of combined data from multiple sample surveys.
abstract: 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.
authors.affiliation: Statistics and Epidemiology Research Unit, RTI International, Triangle Park, NC 27709-2194, USA. lei@rti.org
authors.forename: Lei
authors.initials: L
authors.lastname: Li
authors.forename: Paul S
authors.initials: PS
authors.lastname: Levy
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001699:Biometry / Q000379:methods*; D003625:Data Collection; D003627:Data Interpretation, Statistical; D004812:Epidemiologic Methods; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D016012:Poisson Distribution; D012044:Regression Analysis; D012307:Risk Factors; D012494:Sampling Studies; D012907:Smoking / Q000401:mortality; D012984:Software; D014481:United States / Q000453:epidemiology