The joint modeling of longitudinal and time-to-event data has exploded in the methodological literature in the past decade; however, the availability of software to implement the methods lags behind. The most common form of joint model assumes that the association between the survival and longitudinal...

Competing risks occur in survival analysis when a subject is at risk of more than one type of event. A classic example is when there is consideration of different causes of death. Interest may lie in the cause-specific hazard rates, which can be estimated using standard survival techniques by...

We present the Stata package stgenreg for the parametric analysis of survival data. Any user-defined hazard or log hazard function can be specified, with the model estimated using maximum likelihood utilizing numerical quadrature. Standard parametric models (for example, the Weibull proportional...

Cure models can be used to simultaneously estimate the proportion of cancer patients who are eventually cured of their disease and the survival of those who remain "uncured". One limitation of parametric cure models is that the functional form of the survival of the "uncured" has to be...

In population-based cancer studies, cure is said to occur when the mortality (hazard) rate in the diseased group of individuals returns to the same level as that expected in the general population. The cure fraction (the proportion of patients cured of disease) is of interest to patients and a...