We are interested in survival analysis of hemodialysis patients for whom several biomarkers are recorded over time. Motivated by this challenging problem, we propose a general framework for multivariate joint longitudinal-survival modeling that can be used to examine the association between several longitudinally recorded covariates and a time-to-event endpoint. Our method allows for simultaneous modeling of longitudinal covariates by taking their correlation into account. This leads to a more efficient method for modeling their trajectories over time, and hence, it can better capture their relationship to the survival outcomes
Joint modeling of longitudinal and survival data has become increasingly useful for analyzing clinic...
University of Minnesota Ph.D. dissertation. August 2011. Major: Biostatistics. Advisor: Brad Carlin....
Indiana University-Purdue University Indianapolis (IUPUI)Epidemiologic and clinical studies routinel...
In studying the progression of a disease and to better predict time to death (survival data), invest...
We propose a flexible joint longitudinal-survival framework to examine the association between longi...
Motivated by the United States Renal Data System (USRDS), we propose a joint modeling framework for ...
In longitudinal studies, clinicians usually collect longitudinal biomarkers’ measurements over time ...
In many follow‐up studies different types of outcomes are collected including longitudinal measureme...
In the past couple of decades, longitudinal and survival data analysis have emerged as important and...
In clinical studies, longitudinal and survival data are often obtained simultaneously from the same ...
In analytical studies of longitudinal and time-to-event data, measuring the relationship between lon...
The potentially complex association between a longitudinal biomarker and a time-to-event process, of...
Joint modeling of longitudinal and survival data has received much attention and is becoming increas...
The aim of this presentation is to introduce joint modelling techniques for the simultaneous analysi...
Background Available methods for the joint modelling of longitudinal and time-to-event outcomes have...
Joint modeling of longitudinal and survival data has become increasingly useful for analyzing clinic...
University of Minnesota Ph.D. dissertation. August 2011. Major: Biostatistics. Advisor: Brad Carlin....
Indiana University-Purdue University Indianapolis (IUPUI)Epidemiologic and clinical studies routinel...
In studying the progression of a disease and to better predict time to death (survival data), invest...
We propose a flexible joint longitudinal-survival framework to examine the association between longi...
Motivated by the United States Renal Data System (USRDS), we propose a joint modeling framework for ...
In longitudinal studies, clinicians usually collect longitudinal biomarkers’ measurements over time ...
In many follow‐up studies different types of outcomes are collected including longitudinal measureme...
In the past couple of decades, longitudinal and survival data analysis have emerged as important and...
In clinical studies, longitudinal and survival data are often obtained simultaneously from the same ...
In analytical studies of longitudinal and time-to-event data, measuring the relationship between lon...
The potentially complex association between a longitudinal biomarker and a time-to-event process, of...
Joint modeling of longitudinal and survival data has received much attention and is becoming increas...
The aim of this presentation is to introduce joint modelling techniques for the simultaneous analysi...
Background Available methods for the joint modelling of longitudinal and time-to-event outcomes have...
Joint modeling of longitudinal and survival data has become increasingly useful for analyzing clinic...
University of Minnesota Ph.D. dissertation. August 2011. Major: Biostatistics. Advisor: Brad Carlin....
Indiana University-Purdue University Indianapolis (IUPUI)Epidemiologic and clinical studies routinel...