Sensitivity analysis of misclassification: a graphical and a Bayesian approach.
OBJECTIVE
Misclassification can produce bias in measures of association. Sensitivity analyses have been suggested to explore the impact of such bias, but do not supply formally justified interval estimates.


METHODS
To account for exposure misclassification, recently developed Bayesian approaches were extended to incorporate prior uncertainty and correlation of sensitivity and specificity. Under nondifferential misclassification, a contour plot is used to depict relations among the corrected odds ratio, sensitivity, and specificity.


RESULTS
Methods are illustrated by application to a case-control study of cigarette smoking and invasive pneumococcal disease while varying the distributional assumptions about sensitivity and specificity. Results are compared with those of conventional methods, which do not account for misclassification, and a sensitivity analysis, which assumes fixed sensitivity and specificity.


CONCLUSIONS
By using Bayesian methods, investigators can incorporate uncertainty about misclassification into probabilistic inferences.
Chu
Haitao
H
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
Wang
Zhaojie
Z
Cole
Stephen R
SR
Greenland
Sander
S
D001499:Bayes Theorem; D015982:Bias; D016022:Case-Control Studies; D004781:Environmental Exposure / Q000706:statistics & numerical data; D004812:Epidemiologic Methods; D006801:Humans; D008962:Models, Theoretical; D016017:Odds Ratio; D011008:Pneumococcal Infections / Q000453:epidemiology; D012907:Smoking / Q000453:epidemiology