Joint models for longitudinal and time-to-event data constitute an attractive modeling framework that has received a lot of interest in the recent years. This paper presents the capabilities of the R package JMbayes for fitting these models under a Bayesian approach using Markov chain Monte Carlo algorithms. JMbayes can fit a wide range of joint models, including among others joint models for continuous and categorical longitudinal responses, and provides several options for modeling the association structure between the two outcomes. In addition, this package can be used to derive dynamic predictions for both outcomes, and offers several tools to validate these predictions in terms of discrimination and calibration. All these features are ...
In epidemiologic and clinical studies, a relatively large number of biomarkers are repeatedly measur...
Joint modeling is a collection of statistical methods to properly handle a longitudinal response whi...
Invited presentation by the Statistics Research Group at the Department of Mathematical Sciences, Un...
Joint models for longitudinal and time-to-event data constitute an attractive modeling framework tha...
In longitudinal studies measurements are often collected on different types of outcomes for each sub...
In longitudinal studies measurements are often collected on different types of outcomes for each sub...
This paper is devoted to the R package JSM which performs joint statistical modeling of survival and...
BackgroundIn clinical research, there is an increasing interest in joint modelling of longitudinal a...
Extensions in the field of joint modeling of correlated data and dynamic predictions improve the dev...
In biostatistics and medical research, longitudinal data are often composed of repeated assessments ...
Many clinical trials and other medical studies generate both longitudinal (repeated measurements) an...
Background: Joint modelling of longitudinal and time-to-event outcomes has received considerable att...
The aim of this presentation is to introduce joint modelling techniques for the simultaneous analysi...
Joint models for longitudinal and survival data have gained a lot of attention in recent years, with...
Thesis title: Development and Application of Joint Modelling of Longitudinal and Event-Time Data in ...
In epidemiologic and clinical studies, a relatively large number of biomarkers are repeatedly measur...
Joint modeling is a collection of statistical methods to properly handle a longitudinal response whi...
Invited presentation by the Statistics Research Group at the Department of Mathematical Sciences, Un...
Joint models for longitudinal and time-to-event data constitute an attractive modeling framework tha...
In longitudinal studies measurements are often collected on different types of outcomes for each sub...
In longitudinal studies measurements are often collected on different types of outcomes for each sub...
This paper is devoted to the R package JSM which performs joint statistical modeling of survival and...
BackgroundIn clinical research, there is an increasing interest in joint modelling of longitudinal a...
Extensions in the field of joint modeling of correlated data and dynamic predictions improve the dev...
In biostatistics and medical research, longitudinal data are often composed of repeated assessments ...
Many clinical trials and other medical studies generate both longitudinal (repeated measurements) an...
Background: Joint modelling of longitudinal and time-to-event outcomes has received considerable att...
The aim of this presentation is to introduce joint modelling techniques for the simultaneous analysi...
Joint models for longitudinal and survival data have gained a lot of attention in recent years, with...
Thesis title: Development and Application of Joint Modelling of Longitudinal and Event-Time Data in ...
In epidemiologic and clinical studies, a relatively large number of biomarkers are repeatedly measur...
Joint modeling is a collection of statistical methods to properly handle a longitudinal response whi...
Invited presentation by the Statistics Research Group at the Department of Mathematical Sciences, Un...