In nephrology, a great deal of information is measured repeatedly in patients over time, often alongside data on events of clinical interest. In this introductory article we discuss how these two types of data can be simultaneously analysed using the joint model (JM) framework, illustrated by clinical examples from nephrology. As classical survival analysis and linear mixed models form the two main components of the JM framework, we will also briefly revisit these techniques
In longitudinal studies, clinicians usually collect longitudinal biomarkers’ measurements over time ...
Joint models for longitudinal and survival data are particularly relevant to many cancer clinical tr...
Joint models for longitudinal and survival data are particularly relevant to many cancer clinical tr...
Backgound: The term ‘joint modelling ’ is used in the statistical literature to refer to meth-ods fo...
Backgound: The term ‘joint modelling ’ is used in the statistical literature to refer to meth-ods fo...
Backgound: The term ‘joint modelling’ is used in the statistical literature to refer to methods for ...
Joint modelling techniques have seen great advances in the recent years, with several types of joint...
Mixed types of multivariate outcomes are common in clinical investigations. Survival time is one of ...
<p>A joint model is an analytical method for the simultaneous estimation of treatment effects on sur...
Nephrologists and kidney disease researchers are often interested in monitoring how patients' clinic...
The joint modeling of longitudinal and time-to-event data has exploded in the methodological literat...
In clinical studies, longitudinal and survival data are often obtained simultaneously from the same ...
In longitudinal studies measurements are often collected on different types of outcomes for each sub...
This research analyses the joint behaviour of mortality in different populations, and aims to model ...
In longitudinal studies measurements are often collected on different types of outcomes for each sub...
In longitudinal studies, clinicians usually collect longitudinal biomarkers’ measurements over time ...
Joint models for longitudinal and survival data are particularly relevant to many cancer clinical tr...
Joint models for longitudinal and survival data are particularly relevant to many cancer clinical tr...
Backgound: The term ‘joint modelling ’ is used in the statistical literature to refer to meth-ods fo...
Backgound: The term ‘joint modelling ’ is used in the statistical literature to refer to meth-ods fo...
Backgound: The term ‘joint modelling’ is used in the statistical literature to refer to methods for ...
Joint modelling techniques have seen great advances in the recent years, with several types of joint...
Mixed types of multivariate outcomes are common in clinical investigations. Survival time is one of ...
<p>A joint model is an analytical method for the simultaneous estimation of treatment effects on sur...
Nephrologists and kidney disease researchers are often interested in monitoring how patients' clinic...
The joint modeling of longitudinal and time-to-event data has exploded in the methodological literat...
In clinical studies, longitudinal and survival data are often obtained simultaneously from the same ...
In longitudinal studies measurements are often collected on different types of outcomes for each sub...
This research analyses the joint behaviour of mortality in different populations, and aims to model ...
In longitudinal studies measurements are often collected on different types of outcomes for each sub...
In longitudinal studies, clinicians usually collect longitudinal biomarkers’ measurements over time ...
Joint models for longitudinal and survival data are particularly relevant to many cancer clinical tr...
Joint models for longitudinal and survival data are particularly relevant to many cancer clinical tr...