We consider a competing risks setting, when evaluating the prognostic influence of an exposure on a specific cause of failure. Two main regression models are used in such analyses, the Cox cause-specific proportional hazards model and the subdistribution proportional hazards model. They are exemplified in a real data example focusing on relapse-free interval in acute leukaemia patients. We examine the properties of the estimator based on the latter model when the true model is the former. An explicit relationship between subdistribution hazards ratio and cause-specific hazards ratio is derived, assuming a flexible parametric distribution for latent failure times. Copyright (c) 2006 John Wiley & Sons, Ltd
The Fine-Gray proportional subdistribution hazards model has been puzzling many people since its int...
Prognostic studies often involve modeling competing risks, where an individual can experience only o...
In the competing risks model, a unit is exposed to several risks at the same time, but it is assumed...
We consider a competing risks setting, when evaluating the prognostic influence of an exposure on a ...
In survival analysis, the failure time of an event is interval-censored when the event is only known...
International audienceTo test the effect of a therapeutic or prognostic factor on the occurrence of ...
In survival analyses, competing risks are encountered where the subjects under study are at risk for...
Abstract Background The analysis of time-to-event data can be complicated by competing risks, which ...
The possible occurrence of multiple events during follow-up is a common situation in several clinica...
With competing risks failure time data, one often needs to assess the covariate effects on the cumul...
In this paper, we consider incomplete survival data that is, partly-interval failure time data where...
Clinical trials and cohort studies that collect survival data frequently involve patients who may fa...
Survival analysis focuses nn modelling time to failure from a single cause of failure. In many situa...
Clinical research usually involves time-to-event survival analysis, in which the presence of a compe...
The Fine-Gray proportional subdistribution hazards model has been puzzling many people since its int...
Prognostic studies often involve modeling competing risks, where an individual can experience only o...
In the competing risks model, a unit is exposed to several risks at the same time, but it is assumed...
We consider a competing risks setting, when evaluating the prognostic influence of an exposure on a ...
In survival analysis, the failure time of an event is interval-censored when the event is only known...
International audienceTo test the effect of a therapeutic or prognostic factor on the occurrence of ...
In survival analyses, competing risks are encountered where the subjects under study are at risk for...
Abstract Background The analysis of time-to-event data can be complicated by competing risks, which ...
The possible occurrence of multiple events during follow-up is a common situation in several clinica...
With competing risks failure time data, one often needs to assess the covariate effects on the cumul...
In this paper, we consider incomplete survival data that is, partly-interval failure time data where...
Clinical trials and cohort studies that collect survival data frequently involve patients who may fa...
Survival analysis focuses nn modelling time to failure from a single cause of failure. In many situa...
Clinical research usually involves time-to-event survival analysis, in which the presence of a compe...
The Fine-Gray proportional subdistribution hazards model has been puzzling many people since its int...
Prognostic studies often involve modeling competing risks, where an individual can experience only o...
In the competing risks model, a unit is exposed to several risks at the same time, but it is assumed...