A Bayesian approach to adjust for diagnostic misclassification between two mortality causes in Poisson regression.
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.
Stamey
James D
JD
Department of Statistical Science, Baylor University, Waco, TX 76798-7140, USA. James_Stamey@baylor.edu
Young
Dean M
DM
Seaman
John W
JW
D001499:Bayes Theorem; D002423:Cause of Death; D002675:Child, Preschool; D003198:Computer Simulation; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D015233:Models, Statistical; D009369:Neoplasms / Q000401:mortality; D016012:Poisson Distribution; D012141:Respiratory Tract Infections / Q000401:mortality