|
|
|
|
|
| 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 |
| |
|
|
| Kim, Lois G.; White, Ian R.. |
| Survival data are most frequently analyzed by the intention-to-treat principle. However, presenting a compliance-adjusted analysis alongside the primary analysis can provide an insight into the effect of the treatment for those individuals actually complying with their randomized intervention. There are a number of methods for this type of analysis. Loeys and Goetghebeur (2003) use proportional hazards techniques to provide an estimate of the treatment effect for compliers when compliance is measured on an all-or-nothing scale. This methodology is here made available through a new Stata command, stcomply. |
| Tipo: Journal Article |
Palavras-chave: Stcomply; Compliance; Proportional hazards; Research Methods/ Statistical Methods. |
| Ano: 2004 |
URL: http://purl.umn.edu/116246 |
| |
|
|
|
