A Bayesian approach to adjust for diagnostic misclassification between two mortality causes in Poisson regression
Article first published online: 5 NOV 2007
Copyright © 2007 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 27, Issue 13, pages 2440–2452, 15 June 2008
How to Cite
Stamey, J. D., Young, D. M. and Seaman, J. W. (2008), A Bayesian approach to adjust for diagnostic misclassification between two mortality causes in Poisson regression. Statist. Med., 27: 2440–2452. doi: 10.1002/sim.3134
- Issue published online: 5 MAY 2008
- Article first published online: 5 NOV 2007
- Manuscript Accepted: 4 OCT 2007
- Manuscript Received: 19 FEB 2007
- count data;
Response misclassification of counted data biases and understates the uncertainty of parameter estimators in Poisson regression models. To correct these problems, researchers have devised classical procedures that rely on asymptotic distribution results and supplemental validation data in order to estimate unknown misclassification parameters. We derive a new Bayesian Poisson regression procedure that accounts and corrects for misclassification for a count variable with two categories. Under the Bayesian paradigm, one can use validation data, expert opinion, or a combination of these two approaches to correct for the consequences of misclassification. The Bayesian procedure proposed here yields an operationally effective way to correct and account for misclassification effects in Poisson count regression models. We demonstrate the performance of the model in a simulation study. Additionally, we analyze two real-data examples and compare our new Bayesian inference method that adjusts for misclassification with a similar analysis that ignores misclassification. Copyright © 2007 John Wiley & Sons, Ltd.