In the past two decades, joint models of longitudinal and survival data have received much
attention in the literature. These models are often desirable in the following situations:(i) …

In longitudinal studies it is often of interest to investigate how a marker that is repeatedly
measured in time is associated with a time to an event of interest, eg, prostate cancer studies …

Missing data affect nearly every discipline by complicating the statistical analysis of collected
data. But since the 1990s, there have been important developments in the statistical …

Modelling Survival Data in Medical Research, Fourth Edition, describes the analysis of
survival data, illustrated using a wide range of examples from biomedical research. Written …

In longitudinal studies measurements are often collected on different types of outcomes for
each subject. These may include several longitudinally measured responses (such as blood …

In longitudinal studies it is often of interest to investigate how a marker that is repeatedly
measured in time is associated with a time to an event of interest. This type of research …

Although standard mixed effects models are useful in a range of studies, other approaches
must often be used in correlation with them when studying complex or incomplete data …

This volume examines modern techniques and research problems in the analysis of lifetime
data analysis. This area of statistics deals with time-to-event data which is complicated not …

Motivated by a real data example on renal graft failure, we propose a new semiparametric
multivariate joint model that relates multiple longitudinal outcomes to a time‐to‐event. To …

In medical studies, time-to-event outcomes such as time to death or relapse of a disease are
routinely recorded along with longitudinal data that are observed intermittently during the …