Yiannoutsos, ConstantinBakoyannis, GiorgiosMpofu, Philani BrianTu, WanzhuSong, Yiqing2020-03-112020-03-112020-02https://hdl.handle.net/1805/22280http://dx.doi.org/10.7912/C2/2810Indiana University-Purdue University Indianapolis (IUPUI)In studies with competing risks outcomes, misidentifying the event-type responsible for the observed failure is, by definition, an act of misclassification. Several authors have established that such misclassification can bias competing risks statistical analyses, and have proposed statistical remedies to aid correct modeling. Generally, these rely on adjusting the estimation process using information about outcome misclassification, but invariably assume that outcome misclassification is non-differential among study subjects regardless of their individual characteristics. In addition, current methods tend to adjust for the misclassification within a semi-parametric framework of modeling competing risks data. Building on the existing literature, in this dissertation, we explore the parametric modeling of competing risks data in the presence of outcome misclassification, be it differential or non-differential. Specifically, we develop parametric pseudo-likelihood-based approaches for modeling cause-specific hazards while adjusting for misclassification information that is obtained either through data internal or external to the current study (respectively, internal or external-validation sampling). Data from either type of validation sampling are used to model predictive values or misclassification probabilities, which, in turn, are used to adjust the cause-specific hazard models. We show that the resulting pseudo-likelihood estimates are consistent and asymptotically normal, and verify these theoretical properties using simulation studies. Lastly, we illustrate the proposed methods using data from a study involving people living with HIV/AIDS (PLWH)in the East-African consortium of the International Epidemiologic Databases for the Evaluation of HIV/AIDS (IeDEA EA). In this example, death is frequently misclassified as disengagement from care as many deaths go unreported to health facilities caring for these patients. In this application, we model the cause-specific hazards of death and disengagement from care among PLWH after they initiate anti-retroviral treatment, while adjusting for death misclassification.en-USCompeting RisksHIV OutcomesMisclassificationPseudo-likelihoodSurvival analysisDissertation