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Abstract
One of the fundamental assumptions in survival analysis is that it enable a straight forward interpretation of hazard rates of subject’s covariate(s) on some reference categories or in situations where variables are continuous in nature, the hazard rates must be constant through time. This is also known as the proportional hazard assumption for cox regression. This assumption is always violated in medical practice where subject’s vital statistics or measures are mostly varying, as their medical situations changes with time. This paper therefore develops a Piece-wise survival model, where three levels of Weibull distribution were assumed for baseline hazards. The sensitivity of the baselines were accessed under several censoring percentages (0%, 25%, 50%, and 75%) and sample sizes (n=100, n=500 and n=1000) when models were Single Parametric (SPM) and Partitioned – Piece wise Parametric Model (PPM). A Piece-wise Bayesian hazard model with structured additive predictors in which the functional form of time varying covariate incorporated in a non-proportional hazards framework was also developed, capable of incorporating complex situations in a more flexible framework. Analysis was done utilizing MCMC simulation technique. Results revealed on comparison that the PPM outperformed the SPM with smaller DIC values and larger predictive powers with the LPML criterion and consistently so throughout all simulations.