Multilevel multiprocess hazard models are routinely used by demographers to control for endogeneity and selection effects. These models consist of multilevel proportional hazards equations, and possibly probit equations, with correlated random effects. Although Stata currently lacks a...

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...

In this article, we introduce the stthreg package of Stata commands to fit the threshold regression model, which is based on the first hitting time of a boundary by the sample path of a Wiener diffusion process and is well suited to applications involving time-to-event and survival data. The...

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...

Royston and Parmar (2002, Statistics in Medicine 21: 2175 – 2197) developed a class of flexible parametric survival models that were programmed in Stata with the stpm command (Royston, 2001, Stata Journal 1:1-28). In this article, we introduce a new command, stpm2, that extends the...

In this paper, we describe a new Stata command, stlh, which estimates and tests for the significance of the time-varying regression coefficients in Aalen's linear hazards model; see Aalen (1989). We see two potential uses for this command. One may use it as an alternative to a proportional...

This article describes the stgest command, which implements G-estimation (as proposed by Robins) to estimate the effect of a time-varying exposure on survival time, allowing for time-varying confounders. Copyright 2002 by Stata Corporation.

Longitudinal studies in which exposures, confounders, and outcomes are measured repeatedly over time have the potential to allow causal inferences about the effects of exposure on outcome. There is particular interest in estimating the causal effects of medical treatments (or other...

With the release of Stata 7, the glm command for fitting generalized linear models underwent a substantial overhaul. Stata 7 glm contains an expanded array of variance estimators, regression diagnostics, and other enhancements. The overhaul took place to coincide with the release of Hardin and...