Cure models have been developed to analyze failure time data with a cured fraction. For such data, standard survival models are usually not appropriate because they do not account for the possibility of cure. Mixture cure models assume that the studied population is a mixture of susceptible individuals, who may experience the event of interest, and non-susceptible individuals that will never ex...

We consider nonparametric estimation of cure-rate based on mixture model under Case-1 interval censoring. We show that the nonparametric maximum-likelihood estimator (NPMLE) of cure-rate is non-unique as well as inconsistent , and propose two estimators based on the NPMLE of the distribution function under this censoring model. We present a cross-validation method for choosing a 'cut-off' point...

Cure models are survival models basically developed to estimate the proportion of patients cured in a clinical trial. These models estimate the cured proportion and also the probability of survival. Cure models are a special type of survival analysis model where it is assumed that there are a proportion of subjects who will never experience the event and thus the survival curve will eventually ...

background: birth weight and gestational age are two important variables in obstetric research. the primary measure of gestational age is based on a mother's recall of her last menstrual period. this recall may cause random or systematic errors. therefore, the objective of this study is to utilize bayesian mixture model in order to identify implausible gestational age.   methods: in this cross-...

We consider a class of cure rate frailty models for multivariate failure time data with a survival fraction. This class is formulated through a transformation on the unknown population survival function. It incorporates random effects to account for the underlying correlation, and includes the mixture cure model and the proportional hazards cure model as two special cases. We develop efficient ...

In the mixture distribution model (for continuous and discrete cases) the density function, f̃ (t) is given by a linear combination of k density functions, fi(t), i= 1, · · · ,k, with non-negative weights pi which must sum to 1.0. We propose a generalization of the mixture model where the weights are not restricted to being constant. The specification of the weight functions is not easy because ...

In smoking cessation clinical trials, subjects commonly experience a series of lapse and recovery episodes of varying lengths. Any quit episode may become permanent, in the sense that the subject stops smoking for good, and any lapse may also become permanent, in the sense that the subject abandons the quit attempt entirely. Individual quit patterns may reflect the effects of treatment and meas...