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...

When estimating patient survival using data collected by populationbased cancer registries, it is common to estimate net survival in a relative-survival framework. Net survival can be estimated using the relative-survival ratio, which is the ratio of the observed survival of the patients (where...

Cancer registries are often interested in estimating net survival (NS), the probability of survival if the cancer under study is the only possible cause of death. Pohar Perme, Stare, and Esteve (2012, Biometrics 68: 113–120) proposed a new estimator of NS based on inverse probability...

In this article, we introduce a new Stata command to estimate the net survival function and the net cumulative hazard. The command includes graphic facilities. It was designed to have a similar syntax to sts, the Stata command dedicated to estimate survival and related functions. We present two...

In this article, we describe strel2, a Stata command for the estimation of excess hazard and relative survival in large population-based datasets. strel2 implements the maximum-likelihood estimation approach developed by Est`eve et al. (1990, Statistics in Medicine 9: 529–538) and assumes that...

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...