International audienceTo test the effect of a therapeutic or prognostic factor on the occurrence of a particular cause of failure in the presence of other causes, the interest has shifted in some studies from the modelling of the cause-specific hazard to that of the subdistribution hazard. We present approximate sample size formulas for the proportional hazards modelling of competing risk subdistribution, considering either independent or correlated covariates. The validity of these approximate formulas is investigated through numerical simulations. Two illustrations are provided, a randomized clinical trial, and a prospective prognostic study
Competing risks occur frequently in survival analysis, and in some cases, the competing risks are no...
In many pharmaceutical studies, non-inferiority clinical trials are usually conducted because of the...
The Fine-Gray subdistribution hazard model has become the default method to estimate the incidence o...
International audienceTo test the effect of a therapeutic or prognostic factor on the occurrence of ...
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...
The possible occurrence of multiple events during follow-up is a common situation in several clinica...
In survival analyses, competing risks are encountered where the subjects under study are at risk for...
This paper extends the QUANTIN et al.'s (1996) Regression Survival Model for testing the proportiona...
The Fine-Gray proportional subdistribution hazards model has been puzzling many people since its int...
Clinical trials and cohort studies that collect survival data frequently involve patients who may fa...
Abstract Background The analysis of time-to-event data can be complicated by competing risks, which ...
Abstract Background In randomised clinical trials involving time-to-event outcomes, the failures con...
In the competing risks model, a unit is exposed to several risks at the same time, but it is assumed...
In survival analysis with competing risks, the treatment effect is typically expressed using cause-s...
Competing risks occur frequently in survival analysis, and in some cases, the competing risks are no...
In many pharmaceutical studies, non-inferiority clinical trials are usually conducted because of the...
The Fine-Gray subdistribution hazard model has become the default method to estimate the incidence o...
International audienceTo test the effect of a therapeutic or prognostic factor on the occurrence of ...
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...
The possible occurrence of multiple events during follow-up is a common situation in several clinica...
In survival analyses, competing risks are encountered where the subjects under study are at risk for...
This paper extends the QUANTIN et al.'s (1996) Regression Survival Model for testing the proportiona...
The Fine-Gray proportional subdistribution hazards model has been puzzling many people since its int...
Clinical trials and cohort studies that collect survival data frequently involve patients who may fa...
Abstract Background The analysis of time-to-event data can be complicated by competing risks, which ...
Abstract Background In randomised clinical trials involving time-to-event outcomes, the failures con...
In the competing risks model, a unit is exposed to several risks at the same time, but it is assumed...
In survival analysis with competing risks, the treatment effect is typically expressed using cause-s...
Competing risks occur frequently in survival analysis, and in some cases, the competing risks are no...
In many pharmaceutical studies, non-inferiority clinical trials are usually conducted because of the...
The Fine-Gray subdistribution hazard model has become the default method to estimate the incidence o...