Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134146/1/sim7048_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134146/2/sim7048.pd
We propose a new method for fitting proportional hazards models with error-prone covariates. Regress...
Abstract Background The appropriate handling of missing covariate data in prognostic modelling studi...
The Cox proportional hazards model is frequently used in medical statistics. The standard methods fo...
In survival analysis, a common assumption is that all subjects will eventually experience the event ...
In Cox regression, it is important to test the proportional hazards assumption and sometimes of inte...
In Cox regression, it is important to test the proportional hazards assumption and sometimes of inte...
We propose a new method for fitting proportional hazards models with error-prone covariates. Regress...
Missing covariate data commonly occur in epidemiological and clinical research, and are often dealt ...
ABSTRACT. We propose a new method for fitting proportional hazards models with error-prone covariate...
Some failure time data come from a population that consists of some subjects who are susceptible to ...
Analysis of matched case-control studies is often complicated by missing data on covariates. Analysi...
BACKGROUND: The appropriate handling of missing covariate data in prognostic modelling studies is ye...
Background: The appropriate handling of missing covariate data in prognostic modelling studies is y...
Interval censored survival data, where the exact event time is only known to lie in an observed time...
Missing data is a common issue in epidemiological databases. Among the different ways of dealing wit...
We propose a new method for fitting proportional hazards models with error-prone covariates. Regress...
Abstract Background The appropriate handling of missing covariate data in prognostic modelling studi...
The Cox proportional hazards model is frequently used in medical statistics. The standard methods fo...
In survival analysis, a common assumption is that all subjects will eventually experience the event ...
In Cox regression, it is important to test the proportional hazards assumption and sometimes of inte...
In Cox regression, it is important to test the proportional hazards assumption and sometimes of inte...
We propose a new method for fitting proportional hazards models with error-prone covariates. Regress...
Missing covariate data commonly occur in epidemiological and clinical research, and are often dealt ...
ABSTRACT. We propose a new method for fitting proportional hazards models with error-prone covariate...
Some failure time data come from a population that consists of some subjects who are susceptible to ...
Analysis of matched case-control studies is often complicated by missing data on covariates. Analysi...
BACKGROUND: The appropriate handling of missing covariate data in prognostic modelling studies is ye...
Background: The appropriate handling of missing covariate data in prognostic modelling studies is y...
Interval censored survival data, where the exact event time is only known to lie in an observed time...
Missing data is a common issue in epidemiological databases. Among the different ways of dealing wit...
We propose a new method for fitting proportional hazards models with error-prone covariates. Regress...
Abstract Background The appropriate handling of missing covariate data in prognostic modelling studi...
The Cox proportional hazards model is frequently used in medical statistics. The standard methods fo...