Description streg performs maximum likelihood estimation for parametric regression survival-time models. streg can be used with singleor multiple-record or singleor multiple-failure st data. Survival models currently supported are exponential, Weibull, Gompertz, lognormal, loglogistic, and generalized gamma. Parametric frailty models and shared-frailty models are also fit using streg. Also see ...

BACKGROUND In developing countries about 3.5% of children aged 0-5 years are victims of severe acute malnutrition (SAM). Once the morbidity has developed the cure process takes variable period depending on various factors. Knowledge of time-to-cure from SAM will enable health care providers to plan resources and monitor the progress of cases with SAM. The current analysis presents modeling time...

Frailty models are very useful for analysing correlated survival data, when observations are clustered into groups or for recurrent events. The aim of this article is to present the new version of an R package called frailtypack. This package allows to fit Cox models and four types of frailty models (shared, nested, joint, additive) that could be useful for several issues within biomedical rese...

Many analysis in epidemiological and prognostic studies and in studies of event history data require methods that allow for unobserved covariates or “frailties”. We consider the shared frailty model in the frame work of parametric proportional hazard model. There are certain assumptions about the distribution of frailty and baseline distribution. Mostly gamma distribution is considered for frai...

Survival analyses are greatly used in medical research especially frailty models which are mostly used to account for heterogeneity in time-to-event. Over the years treatment of cancer has progressed with some patients being cured from different type of cancer. Survival analysis is more focused on subjects that are less at risk of recurrences, metastasis or death after the first treatment as th...

The shared frailty models allow for unobserved heterogeneity or for statistical dependence between observed survival data. The most commonly used estimation procedure in frailty models is the EM algorithm, but this approach yields a discrete estimator of the distribution and consequently does not allow direct estimation of the hazard function. We show how maximum penalized likelihood estimation...