Research in the field of survival analysis has increased tremendiously over the last several decades because of its large usage in areas related to biology, medicine, public health and epidemiology. Cox proportional hazard model is widely applicable to analyze survival data but there are few probability distributions for survival time that can be used with these models. In such a situation the accelerated failure time (AFT) models and proportional odd are alternative to the Cox proportional hazard model for the analysis of the survival time data. Cox proportional hazard (PH) model, accelerated failure time (AFT) model and Proportional odd (PO) model describe the relationship between survival probabilities and set of covariates. We compared flexible nonparametric models with categorical and continuous covariates in time to event data. The models based on Dirichlet process, Polya tree prior and their dependent extension. The models are illustrated by Cancer data.