Date on Master's Thesis/Doctoral Dissertation
Bioinformatics and Biostatistics
Committee Co-Chair (if applicable)
cross-sectional survey; interval censored data; Phase IV clinical trial; transition intensity; illness-death model.
Phase IV clinical trials are designed to monitor long-term side effects caused overtime by the medical treatment. For instance, in advanced primary cancer treatment, childhood cancer survivors are often at risk of developing undesired events, such as cardiotoxicity, during their adulthood. Such problems could be due to their cancer or the treatment they received for their cancer such as radiation or intensive chemotherapy. Cardiotoxicity can be diagnosed with electrophysiology with measurements of fraction shortening, afterload, etc. Often the primary focus of a study could be on estimating the cumulative incidence of a particular outcome of interest such as cardiotoxicity. However, it is not possible to evaluate patients on a continuous basis and often this information is collected through cross-sectional surveys by following patients longitudinally. This leads to interval censored data since the exact time of the onset of toxicity is not known. This dissertation consists of three projects related to the estimation of cumulative incidence rates on interval censored data. In the first project, missing observation problem in the current status data is discussed. An imputation method is proposed to handle such issue. The second project introduces a new method for estimating transition intensity probabilities using semi-parametric with EM algorithm approach in a special case two-state model. A logit relationship for the treatment intensities in the two groups is proposed. In the third project, the cumulative incidence rates are evaluated using the maximum likelihood estimation approach in a complete three-state illness-death model in which death is incorporated as competing risk.
Qian, Chen, "Estimating cumulative incidence rate on interval censored data in an illness-death model." (2021). Electronic Theses and Dissertations. Paper 3636.