Due to the proliferation of biomedical imaging modalities, such as Photoacoustic
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …

Summary Patient-reported outcomes (PROs), such as symptoms, function, and other health-
related quality-of-life aspects, are increasingly evaluated in cancer randomised controlled …

Currently available risk prediction methods are limited in their ability to deal with complex,
heterogeneous, and longitudinal data such as that available in primary care records, or in …

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 …

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 …

INLA stands for Integrated Nested Laplace Approximations, which is a new method for fitting
a broad class of Bayesian regression models. No samples of the posterior marginal …

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 …

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 …

With contributions from some of the most prominent researchers in the field, this carefully
edited collection provides a clear, comprehensive, and unified overview of recent …

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 …