The hazard function plays a central role in survival analysis. In a homogeneous population,
the distribution of the time to event, described by the hazard, is the same for each individual …

Data that have a multilevel structure occur frequently across a range of disciplines, including
epidemiology, health services research, public health, education and sociology. We …

The envelope spike (S) proteins of MERS-CoV and SARS-CoV determine the virus host
tropism and entry into host cells, and constitute a promising target for the development of …

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 …

Temporal point processes are the dominant paradigm for modeling sequences of events
happening at irregular intervals. The standard way of learning in such models is by …

Importance Neighborhood environments may influence the risk for developing type 2
diabetes mellitus (T2DM), but, to our knowledge, no longitudinal study has evaluated …

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 …

Wide-Ranging Coverage of Parametric Modeling in Linear and Nonlinear Mixed Effects
Models Mixed Effects Models for the Population Approach: Models, Tasks, Methods and …

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 …

Multistate Models for the Analysis of Life History Data provides the first comprehensive
treatment of multistate modeling and analysis, including parametric, nonparametric and …