Joint modelling

Bayesian shared parameter joint models for heterogeneous populations

Joint models (JMs) for longitudinal and time-to-event data are an important class of biostatistical models in health and medical research. When the study population consists of heterogeneous subgroups, the standard JM may be inadequate and lead to …

WIVE

Looking beyond the mean: what within-person variability can tell us about dementia, cardiovascular disease and cystic fibrosis

A Bayesian location-scale joint model for time-to-event and multivariate longitudinal data with association based on within-individual variability

Within-individual variability of health indicators measured over time is becoming commonly used to inform about disease progression. Simple summary statistics (e.g. the standard deviation for each individual) are often used but they are not suited to …

EPS6.09 Association of within-individual variability of FEV1 and BMI with mortality in women with cystic fibrosis: preliminary results from the UK Registry

Malnutrition and lung function have been associated with mortality in adults with cystic fibrosis (CF), but many longitudinal analyses have focused only on the average trend of FEV1 and BMI over time. Their approach disregards potentially useful …