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 …

Survival analysis arises in many fields of study including medicine, biology, engineering,
public health, epidemiology, and economics. This book provides a comprehensive treatment …

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 …

Accurately evaluating minimal residual disease (MRD) could facilitate early intervention and
personalized adjuvant therapies. Here, using ultradeep targeted next-generation …

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 Available methods for the joint modelling of longitudinal and time-to-event
outcomes have typically only allowed for a single longitudinal outcome and a solitary event …

Explicitly modeling underlying relationships between a survival endpoint and processes that
generate longitudinal measured or reported outcomes potentially could improve the …

Joint modeling of longitudinal and survival data is becoming increasingly essential in most
cancer and AIDS clinical trials. We propose a likelihood approach to extend both …