textabstractIn longitudinal studies measurements are often collected on different types of outcomes for each subject. These may include several longitudinally measured responses (such as blood values relevant to the medical condition under study) and the time at which an event of particular interest occurs (e.g., death, development of a disease or dropout from the study). These outcomes are often separately analyzed; however, in many instances, a joint modeling approach is either required or may produce a better insight into the mechanisms that underlie the phenomenon under study. In this paper we present the R package JM that fits joint models for longitudinal and time-to-event data
Joint modeling of various longitudinal sequences has received quite a bit of attention in recent tim...
• In longitudinal studies subjects are measured for one or more response variable, over time. Althou...
The joint modeling of longitudinal and time-to-event data has received much attention in the biostat...
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...
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...
In biostatistics and medical research, longitudinal data are often composed of repeated assessments ...
In longitudinal studies it is often of interest to investigate how a marker that is repeatedly measu...
The joint modeling of longitudinal and time-to-event data has exploded in the methodological literat...
The aim of this presentation is to introduce joint modelling techniques for the simultaneous analysi...
Joint models for longitudinal and time-to-event data constitute an attractive modeling framework tha...
In clinical and epidemiological studies, very often, observations are collected on more than one cor...
Joint models for longitudinal and time-to-event data constitute an attractive modeling framework tha...
It is common in epidemiology and clinical research to take repeat measurements of a marker (for exam...
Joint modeling of various longitudinal sequences has received quite a bit of attention in recent tim...
• In longitudinal studies subjects are measured for one or more response variable, over time. Althou...
The joint modeling of longitudinal and time-to-event data has received much attention in the biostat...
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...
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...
In biostatistics and medical research, longitudinal data are often composed of repeated assessments ...
In longitudinal studies it is often of interest to investigate how a marker that is repeatedly measu...
The joint modeling of longitudinal and time-to-event data has exploded in the methodological literat...
The aim of this presentation is to introduce joint modelling techniques for the simultaneous analysi...
Joint models for longitudinal and time-to-event data constitute an attractive modeling framework tha...
In clinical and epidemiological studies, very often, observations are collected on more than one cor...
Joint models for longitudinal and time-to-event data constitute an attractive modeling framework tha...
It is common in epidemiology and clinical research to take repeat measurements of a marker (for exam...
Joint modeling of various longitudinal sequences has received quite a bit of attention in recent tim...
• In longitudinal studies subjects are measured for one or more response variable, over time. Althou...
The joint modeling of longitudinal and time-to-event data has received much attention in the biostat...