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| Royston, Patrick. |
| Since its introduction to a wondering public in 1972, the Cox proportional hazards regression model has become an overwhelmingly popular tool in the analysis of censored survival data. However, some features of the Cox model may cause problems for the analyst or an interpreter of the data. They include the restrictive assumption of proportional hazards for covariate effects, and “loss” (non-estimation) of the baseline hazard function induced by conditioning on event times. In medicine, the hazard function is often of fundamental interest since it represents an important aspect of the time course of the disease in question. In the present article, the Stata implementation of a class of flexible parametric survival models recently proposed by Royston and... |
| Tipo: Journal Article |
Palavras-chave: Parametric survival analysis; Hazard function; Proportional hazards; Proportional odds; Research Methods/ Statistical Methods. |
| Ano: 2001 |
URL: http://purl.umn.edu/115931 |
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| Gutierrez, Roberto G.. |
| Frailty models are the survival data analog to regression models, which account for heterogeneity and random effects. A frailty is a latent multiplicative effect on the hazard function and is assumed to have unit mean and variance θ, which is estimated along with the other model parameters. A frailty model is an heterogeneity model where the frailties are assumed to be individual- or spell-specific. A shared frailty model is a random effects model where the frailties are common (or shared) among groups of individuals or spells and are randomly distributed across groups. Parametric frailty models were made available in Stata with the release of Stata 7, while parametric shared frailty models were made available in a recent series of updates. This article... |
| Tipo: Journal Article |
Palavras-chave: Parametric survival analysis; Frailty; Random effects; Overdispersion; Heterogeneity; Research Methods/ Statistical Methods. |
| Ano: 2002 |
URL: http://purl.umn.edu/115948 |
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