BACKGROUND Misclassification of exposure variables in epidemiologic studies may lead to biased estimation of parameters and loss of power in statistical inferences. In this paper, the inverse matrix method, as an efficient method of the correction of odds ratio for the misclassification of a binary exposure, was generalized to nondifferential misclassification and 2 × 2 × J tables. METHODS Si...

In data mining and in classification specifically, cost issues have been undervalued for a long time, although they are of crucial importance in real-world applications. Recently, however, cost issues have received growing attention, see for example [1,2,3]. Cost-sensitive classifiers are usually based on the assumption of constant misclassification costs between given classes, that is, the cos...

Aim The aim of this study was to investigate the impact of diabetes and hypertension on colorectal cancer (CRC) mortality. Background One of the methodology in epidemiological studies is to use self-report questionnaires to gather data, this is the easiest and cheapest method but involves with misclassification bias. We use robust Bayesian adjustment to correct this bias. Methods One of the...

INTRODUCTION The validity of self-reported data for mammography differ by race. We assessed the effect of racial differences in the validity of age-adjusted, self-reported mammography use estimates from the Behavioral Risk Factor Surveillance System (BRFSS) from 1995 through 2006 to determine whether misclassification (inaccurate survey question response) may have obscured actual racial dispari...

Cohort studies and clinical trials may involve multiple events. When occurrence of one of these events prevents the observance of another, the situation is called "competing risks". A useful measure in such studies is the cumulative incidence of an event, which is useful in evaluating interventions or assessing disease prognosis. When outcomes in such studies are subject to misclassification, t...