In this paper, we consider the joint modelling of survival and longitudinal data with informative observation time points. The survival model and the longitudinal model are linked via random effects, for which no distribution assumption is required under our estimation approach. The estimators are shown to be consistent and asymptotically normal. The proposed estimator and its estimated covariance matrix can be easily calculated. Simulation studies and an application to a primary biliary cirrhosis study are also provided
Invited presentation by the Statistics Research Group at the Department of Mathematical Sciences, Un...
In longitudinal studies it is often of interest to measure the association between a longitudinal ma...
In analysis of longitudinal data, a number of methods have been proposed. Most of the traditional lo...
Although longitudinal and survival data are collected in the same study, they are usually analyzed s...
In the past couple of decades, longitudinal and survival data analysis have emerged as important and...
The joint modeling of longitudinal and survival data has received remarkable attention in the method...
The joint modeling of longitudinal and survival data is a new approach to many applications such as ...
In analytical studies of longitudinal and time-to-event data, measuring the relationship between lon...
This paper is devoted to the R package JSM which performs joint statistical modeling of survival and...
Often the motivation behind building a statistical model is to provide prediction for an outcome of ...
Monitoring of individual biomarkers has the potential of explaining the hazard of survival outcomes....
It is common in longitudinal studies to collect information on the time until a key clinical event, ...
In some fields of biometrical research joint modelling of longitudinal measures and event time data ...
In studying the progression of a disease and to better predict time to death (survival data), invest...
2012-2013 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe
Invited presentation by the Statistics Research Group at the Department of Mathematical Sciences, Un...
In longitudinal studies it is often of interest to measure the association between a longitudinal ma...
In analysis of longitudinal data, a number of methods have been proposed. Most of the traditional lo...
Although longitudinal and survival data are collected in the same study, they are usually analyzed s...
In the past couple of decades, longitudinal and survival data analysis have emerged as important and...
The joint modeling of longitudinal and survival data has received remarkable attention in the method...
The joint modeling of longitudinal and survival data is a new approach to many applications such as ...
In analytical studies of longitudinal and time-to-event data, measuring the relationship between lon...
This paper is devoted to the R package JSM which performs joint statistical modeling of survival and...
Often the motivation behind building a statistical model is to provide prediction for an outcome of ...
Monitoring of individual biomarkers has the potential of explaining the hazard of survival outcomes....
It is common in longitudinal studies to collect information on the time until a key clinical event, ...
In some fields of biometrical research joint modelling of longitudinal measures and event time data ...
In studying the progression of a disease and to better predict time to death (survival data), invest...
2012-2013 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe
Invited presentation by the Statistics Research Group at the Department of Mathematical Sciences, Un...
In longitudinal studies it is often of interest to measure the association between a longitudinal ma...
In analysis of longitudinal data, a number of methods have been proposed. Most of the traditional lo...