Joint Modeling of Longitudinal and Time-to-Event Data

Robert Elashoff, Gang Li, Ning Li

"This book is a comprehensive state-of-the-art treatment of joint models for time-to-event and longitudinal data with numerous applications to real-world problems. … [T]his book is a comprehensive review of the existing literature on joint models, including most extensions of these models, fully parametric or not, for multiple events and multiple markers with a special focus on missingness problems and details about various estimation methods. By emphasizing the most advanced methods, this book usefully completes existing monographs on joint models and will be a helpful reference book for researchers in biostatistics and experienced statisticians, while applied statisticians could also be interested thanks to the numerous examples of real data analyses."—Helene Jacqmin-Gadda, University of Bordeaux, in Biometrics, March 2018"This book provides an extensive survey of research performed on the subject of joint models in longitudinal and time-to-event data. … The authors’ expertise in this area shines through their careful attention to detail in presenting the wide variety of settings in which these models can be applied. Overall, I consider the book to be a valuable and rich resource for introducing and promoting this relatively new area of research. … Where this book primarily succeeds is in the great care taken by the authors in walking through the necessary details of these joint models and the breadth of topics they cover. When topics are left out, the authors refer to a large body of literature to which the interested reader can look to further their understanding. … I would recommend it either as a handy reference for researchers or as a graduate level reference text in a specialized course … [I]t is truly rich with useful content that can be extracted and applied with due diligence. …. I certainly consider it a valuable addition to my bookshelf for personal reference and, should the need arise, I would be happy to refer it to

• Introduction and ExamplesIntroductionMethods for Ignorable Missing DataIntroductionMissing Data MechanismsLinear and Generalized Linear Mixed ModelsGeneralized Estimating EquationsFruther topicsTime-to-event data analysisRight censoringSurvival function and hazard functionEstimation of a survival functionCox's semiparametric multiplicative hazards modelsAccelerated failure time models with time-independent covariatesAccelerated failure time model with time-dependent covariatesMethods for competing risks dataFurther topicsOverview of Joint Models for Longitudinal and Time-to-Event DataJoint Models of Longitudinal Data and an Event timeJoint Models with Discrete Event Times and Monotone MissingnessLongitudinal Data with Both Monotone and Intermittent Missing ValuesEvent Time Models with Intermittently Measured Time Dependent CovariatesLongitudinal Data with Informative Observation TimesDynamic Prediction in Joint ModelsJoint Models for Longitudinal Data and Continuous Event Times from Competing RisksJoint Alaysis of Longitudinal Data and Competing RisksA Robust Model with t-Distributed Random ErrorsOrdinal Longitudinal Outcomes with Missing Data Due to Multiple Failure TypesBayesian Joint Models with Heterogeneous Random EffectsAccelerated Failure Time Models for Competing RisksJoint Models for Multivariate Longitudinal and Survival DataJoint Models for Multivariate Longitudinal Outcomes and an Event TimeJoint Models for Recurrent Events and Longitudinal DataJoint Models for Multivariate Survival and Longitudinal DataFurther TopicsJoint Models and Missing Data: Assumptions, Sensitivity Analysis, and DiagnosticsVariable Selection in Joint ModelsJoint Multistate ModelsJoint Models for Cure Rate Survival DataSample Size and Power Estimation for Joint ModelsAppendicesA Software to Implement Joint ModelsBibliographyIndex