When the mortality among a cancer patient group returns to the same level as in the general population, that is, when the patients no longer experi- ence excess mortality, the patients still alive are considered “statistically cured”. Cure models can be used to estimate the cure proportion...

It is usual in time-to-event data to have more than one event of interest, for example, time to death from different causes. Competing risks models can be applied in these situations where events are considered mutually exclusive absorbing states. That is, we have some initial state—for...

Age–period–cohort models provide a useful method for modeling incidence and mortality rates. It is well known that age–period–cohort models suffer from an identifiability problem due to the exact relationship between the variables (cohort = period − age). In 2007, Carstensen published...

Competing risks are present when the patients within a dataset could experience one or more of several exclusive events and the occurrence of any one of these could impede the event of interest. One of the measures of interest for analyses of this type is the cumulative incidence function....

Simulation studies are essential for understanding and evaluating both current and new statistical models. When simulating survival times, one often assumes an exponential or Weibull distribution for the baseline hazard function, with survival times generated using the method of Bender,...

The joint modeling of longitudinal and survival data has received remarkable attention in the methodological literature over 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...

Cure models are a special type of survival analysis model where it is assumed that there are a proportion of sub jects who will never experience the event and thus the survival curve will eventually reach a plateau. In population-based cancer studies, cure is said to occur when the mortality...

The Cox proportional hazards model has been used extensively in medicine over the last 40 years. A popular application is to develop a multivariable prediction model, often a prognostic model to predict the clinical outcome of patients with a particular disorder from "baseline" factors measured...

Royston (2014, Stata Journal 14: 738–755) explained how a popular application of the Cox proportional hazards model "is to develop a multivariable prediction model, often a prognostic model to predict the future clinical outcome of patients with a particular disorder from 'baseline' factors...

I provide a new programming tool, cmpute, to manage conveniently the creation of a new variable or the replacement of an existing variable interactively or within a Stata program. Copyright 2013 by StataCorp LP.