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...

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...

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...

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 an era in which doctors and patients aspire to personalized medicine and more sophisticated risk estimation, detecting and modeling interactions between covariates or between covariates and treatment is increasingly important. In observational studies (for example, in epidemiology),...

This tutorial aims to cover many of the aspects of the flexible parametric alternatives to the Cox model that we have been developing.

All doctors treating patients with Breast Cancer know which key variables indicate a good prognosis and which values decrease the chances of surviving. However because of complex interactions between the variables and survival doctors cannot give an individualized prognosis to a patient. The...

Cox proportional-hazard regression has been essentially the automatic choice of analysis tool for modeling survival data in medical studies. However, the Cox model has several intrinsic features that may cause problems for the analyst or an interpreter of the data. These include the necessity of...