Exploring the health related quality of life is usually the focus of the survival studies. Using the health data of cancer registry in Multan, Pakistan, an investigation about the survival pattern of cancer patients was explored, using the non-parametric and parametric modeling strategies. The Kaplan-Meier method and Weibull model based on Anderson-Darling test were applied to the real life tim...

Survival analysis aims to predict the occurrence of specific events of interest at future time points. The presence of incomplete observations due to censoring brings unique challenges in this domain and differentiates survival analysis techniques from other standard regression methods. In many applications where the distribution of the survival times can be explicitly modeled, parametric survi...

Introduction: There is a lack of information on the extent of dependency between chronic diseases and the survival rate of breast cancer. Until date, none of the models proposed has determined the impact of chronic diseases on breast cancer survival. This study, therefore, aimed to investigate the impacts of chronic diseases such as diabetes, blood pressure, and endocrine di...

Introduction: There is a lack of information on the extent of dependency between chronic diseases and the survival rate of breast cancer. Until date, none of the models proposed has determined the impact of chronic diseases on breast cancer survival. This study, therefore, aimed to investigate the impacts of chronic diseases such as diabetes, blood pressure, and endocrine di...

Given that correct assumptions on the baseline survival function are determinant for the validity of further inferences, specific tools to test the fit of a model to real data become essential in proportional hazards models. In this sense, we have proposed a parametric bootstrap to test the fit of survival models. Monte Carlo simulations are used to generate new data sets from the estimates obt...

Background Cox proportional hazard model is the most common method for analyzing the effects of several variables on survival time. However, under certain circumstances, parametric models give more precise estimates to analyze survival data than Cox. The purpose of this study was to investigate the comparative performance of Cox and parametric models in a survival analysis of factors affecting ...

In Bayesian paradigm of survival analysis, we can combine a nonparametric estimator and a parametric model by putting a prior distribution nonparametrically around the entire parametric family. This method can avoids the ineeciency of the nonparametric estimator due to ignoring partial information about a parametric model and at the same time avoids the pitfalls connected with an incorrectly sp...

We introduce a semi-parametric Bayesian model for survival analysis. The model is centred on a parametric baseline hazard, and uses a Gaussian process to model variations away from it nonparametrically, as well as dependence on covariates. As opposed to many other methods in survival analysis, our framework does not impose unnecessary constraints in the hazard rate or in the survival function. ...

BACKGROUND The current lifetable approach to survival estimation is favoured by CF registries. Recognising the limitation of this approach, we examined the utility of a parametric survival model to project birth cohort survival estimates beyond the follow-up period, where short duration of follow-up meant median survival estimates were indeterminable. METHODS Parametric models were fitted to ...