Shen, HuaLi, Ruoyu2022-11-152020-09-25Li, R. (2020). Competing Risk Analysis with Misclassified Covariates (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.http://hdl.handle.net/1880/115476https://dx.doi.org/10.11575/PRISM/40443Misclassification in categorical variables and missing data can often occur concurrently in medical research. Though there has been extensive research on either topic, relatively little work is available to address both issues simultaneously, especially in survival analysis. In this thesis, we first propose a method for the competing risk analysis involving a latent categorical covariate where validation data is absent and the latent variable of interest is only measured subject to misclassification via a set of surrogate variables. We then extend it to a more general setting where the latent covariate is not measured by the same number of surrogate variables for all subjects.For example, the decision to be measured by additional surrogate variable depends on the available faulty measurements of the latent variable by preceding surrogate variables resulting in a sequential missing pattern among the surrogates. In both cases we apply direct approach in the analysis of competing risks focusing on the cumulative incidence functions of the event of interest and its competing events and adopt flexible parametric forms for the baseline cumulative incidence functions. We develop likelihood-based methods based on expectation-maximization algorithms and jointly model the competing risks, surrogate variables and latent covariate of interest. The procedures simultaneously allow estimation of the covariate effects on the event of interest, parameters in the baseline cumulative incidence functions, regression coefficients in the misclassification model and association between the latent covariate and other completely and precisely observed covariates. We evaluate the empirical performance of the proposed methods in simulation studies. We conclude that they outperform the naive and ad hoc approaches in both cases and are relatively robust to sample size, misclassification rate and missing proportion of the surrogate variables. Finally, we apply the proposed method to the stimulating study on breast cancer. Discussion and future work are outlined in the end.enUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Statisticsmaster thesis