Описание: In follow-up studies it is often of interest to investigate how a longitudinal outcome that is repeatedly measured in time is associated with a time to an event of interest. Typical examples in this setting come from biomarker research, such as HIV studies where longitudinal CD4 cell counts and viral load are collected in conjunction to the time-to-death, and prostate cancer studies where longitudinal PSA level measurements are collected in conjunction with the time-to-recurrence.
This book is the first providing a full treatment of random effects joint models for longitudinal and time-to-event outcomes that can be utilized to analyze such data. The content is primarily explanatory focusing on applications of joint modeling, but sufficient mathematical details are provided to facilitate understanding of the key features of these models. All illustrations put forward can be implemented in the R programming language via the freely available package JM written by the author.
