Joint models for longitudinal and survival data are particularly relevant to many cancer
clinical trials and observational studies in which longitudinal biomarkers (eg, circulating …

Incomplete data are quite common in biomedical and other types of research, especially in
longitudinal studies. During the last three decades, a vast amount of work has been done in …

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 …

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 …

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 …

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

Design Principles and Analysis Techniques for HRQoL Clinical TrialsSAS, R, and SPSS
examples realistically show how to implement methods Focusing on longitudinal studies …

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 …

In this review, we present an overview of joint models for longitudinal and time-to-event data.
We introduce a generalized formulation for the joint model that incorporates multiple …

Background Clinical research and medical practice can be advanced through the prediction
of an individual's health state, trajectory, and responses to treatments. However, the majority …