Bayesian analysis of partly interval censored data under competing risks framework using engineering data - Yousif, Yosra Abd Elrahman Adam (Kulliyyah of Engineering, International Islamic University Malaysia, 2017)
Bayesian approach was developed for regression analysis of partly interval-censored data in presence of masking. The primary objective of this thesis is to assess the effect of risks factors (covariates) on both cause-specific hazard (CSH) function and cumulative incident function (CIF), therefore, the popular Cox's proportional hazards model was utilized as well as Fine & Gray's proportional subdistribution hazards model. Several simulations were conducted with various scenarios in order to evaluate the performance of the proposed models. Further, an engineering data set was employed for the purpose of illustration. The proposed methods are flexible and valid for an arbitrary number of causes, moreover, they are easy to implement. The three developed models are shown to have a good performance throughout the simulation studies. The obtained estimations are accurate and comparable to that result from models with only right-censored data and that without masked causes of failure. The model under CIF framework shows a little sensitivity towards high levels of interval-censored observations. Unlike, the models under CSH formulation show sensitivity towards high levels of masking.