We consider survival data from a population with cured subjects in the presence of mismeasured covariates. use mixture cure model to account for individuals that will never experience event and at same time distinguish between effect covariates on probabilities times. In particular, practical applications, it seems interest assume logistic form incidence Cox proportional hazards latency. To cor...

A challenge when dealing with survival analysis data is accounting for a cure fraction, meaning that some subjects will never experience the event of interest. Mixture models have been frequently used to estimate both probability being cured and time susceptible subjects, by usually assuming parametric (logistic) form incidence. We propose new estimation procedure rate relies on preliminary smo...

Background. Since the first outbreak of COVID-19, most hospitals restricted patients’ family support accompaniment during medical treatment infectious transmission. On other hand, has also been recognized as an essential part treatment. Therefore, this study aims to determine effect presence accompanying COVID-19 patients hospitalization on recovery rate and survival time. Objective: was conduc...

background: breast cancer is the most common cancer after lung cancer and the second cause of death. in this study we compared weibull and lognormal cure models with cox regression on the survival of breast cancer. study design: a cohort study. methods: the current study retrospective cohort study was conducted on 140 patients referred to ali ibn abitaleb hospital, rafsanjan southeastern iran f...

Cure models for clustered survival data have the potential for broad applicability. In this paper, we consider the mixture cure model with random effects and propose several estimation methods based on Gaussian quadrature, rejection sampling, and importance sampling to obtain the maximum likelihood estimates of the model for clustered survival data with a cure fraction. The methods are flexible...

We introduce the Weibull distributions in presence of cure fraction, censored data and covariates. Twomodels are explored in this paper: mixture and non-mixture models. Inferences for the proposed models are obtained under the Bayesian approach, using standard MCMC (Markov Chain Monte Carlo) methods. An illustration of the proposed methodology is given considering a lifetime data set.