Dependent Error Misclassification in both the Response Variable and Covariate.

Errors in Variables (EIV) are a long-standing issue in many fields, including medical and epidemiological studies. Ignoring these errors can produce misleading inferential results. In discrete responses, EIV are commonly termed as misclassi fication errors. Studies on misclassifi cation have mostly focused on misclassification in only one variable. Joint misclassification in both the response variable and the covariate has been less explored. Some literature on joint misclassification assumes the misclassification process of the response variable is independent of the misclassification process of the covariate. However, in practice, the dependence of misclassification errors can occur. For example both, the response variable and covariate are obtained from a similar source as in the case of self-reported responses from a questionnaire. The objective is to investigate (1) modeling for error-prone response variable and error-prone covariate and (2) consequences of using an incorrect misclassification model. In this thesis, we first introduce a model that accounts for dependent misclassification error in a binary response variable and a binary covariate. The dependence of error is captured through covariance-like parameters. Simulation studies are conducted to assess the consequences of fitting an independent misclassification model to data generated from a dependent misclassification model. The simulation experiments have several key factors to manipulate: the amount of misclassification error (sensitivity and specificity), the dependence between the misclassification process of the response variable, and the misclassification process of the covariate, and the proportion of internal validation data. Further, the model is extended to a multi-category setting and simulation study is conducted on a trinary response variable and a trinary covariate. Results from the simulation studies indicate that ignoring dependence of the error in misclassification can be worse than ignoring misclassification. The proposed model is illustrated through a real data example by establishing the true association between Trichomoniasis and Bacterial Vaginosis, using data from the HIV Epidemiology Research Study (HERS). A likelihood-ratio test is proposed to test the independent misclassification assumption. The test concluded that the dependent misclassification error model fits the HERS data significantly better than the model that ignored dependence misclassification