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Regression modeling of combined data from multiple sample surveys
Author(s) -
Li Lei,
Levy Paul S.
Publication year - 2009
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3610
Subject(s) - poisson regression , statistics , logistic regression , computer science , regression analysis , sample (material) , generalized linear model , econometrics , regression , linear regression , sampling (signal processing) , sample size determination , estimation , poisson distribution , population , data mining , mathematics , medicine , engineering , environmental health , chemistry , filter (signal processing) , chromatography , computer vision , systems engineering
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. Copyright © 2009 John Wiley & Sons, Ltd.