A general model for the illness-death stochastic process with covariates has been developed for the analysis of survival data. This model incorporates important baseline and time-dependent covariates to make proper adjustment for the transition probabilities and survival probabilities. The follow-up period is subdivided into small intervals and a constant hazard is assumed for each interval. An approximation formula is derived to estimate the transition parameters when the exact transition time is unknown.^ The method developed is illustrated by using data from a study on the prevention of the recurrence of a myocardial infarction and subsequent mortality, the Beta-Blocker Heart Attack Trial (BHAT). This method provides an analytical app...
Time-to-event data refers to the observed time from a defined origin (e.g. diagnosis of a disease) u...
In many medical studies, there are covariates that change their values over time and their analysis ...
Abstract:In the present article, we discuss a flexi-ble method for modeling censored survival data u...
A general model for the illness-death stochastic process with covariates has been developed for the ...
This dissertation focuses on developing new mathematical and statistical methods to properly represe...
It is common in longitudinal studies to collect information on the time until a key clinical event, ...
Clinical trials are often concerned with the evaluation of two or more time-dependent stochastic eve...
International audienceRecently, there has been a lot of development in relative survival field. In t...
Statistical modeling of lifetime data, or survival analysis, is studied in many fields, including me...
In survival studies the values of some covariates may change over time. It is natural to incorporate...
Abstract One important goal in multi-state modeling is the estimation of transi-tion probabilities. ...
Analyses of human mortality data classified according to cause of death frequently are based on comp...
The thesis concerns regression models related to the competing risks setting in survival analysis an...
One important goal in multi-state modeling is the estimation of transition probabilities. In longitu...
University of Minnesota Ph.D. dissertation. May 2011. Major: Biostatistics. Advisor: Melanie M. Wall...
Time-to-event data refers to the observed time from a defined origin (e.g. diagnosis of a disease) u...
In many medical studies, there are covariates that change their values over time and their analysis ...
Abstract:In the present article, we discuss a flexi-ble method for modeling censored survival data u...
A general model for the illness-death stochastic process with covariates has been developed for the ...
This dissertation focuses on developing new mathematical and statistical methods to properly represe...
It is common in longitudinal studies to collect information on the time until a key clinical event, ...
Clinical trials are often concerned with the evaluation of two or more time-dependent stochastic eve...
International audienceRecently, there has been a lot of development in relative survival field. In t...
Statistical modeling of lifetime data, or survival analysis, is studied in many fields, including me...
In survival studies the values of some covariates may change over time. It is natural to incorporate...
Abstract One important goal in multi-state modeling is the estimation of transi-tion probabilities. ...
Analyses of human mortality data classified according to cause of death frequently are based on comp...
The thesis concerns regression models related to the competing risks setting in survival analysis an...
One important goal in multi-state modeling is the estimation of transition probabilities. In longitu...
University of Minnesota Ph.D. dissertation. May 2011. Major: Biostatistics. Advisor: Melanie M. Wall...
Time-to-event data refers to the observed time from a defined origin (e.g. diagnosis of a disease) u...
In many medical studies, there are covariates that change their values over time and their analysis ...
Abstract:In the present article, we discuss a flexi-ble method for modeling censored survival data u...