Survival Analysis with Interval-Censored Data
A Practical Approach with Examples in R, SAS, and BUGS
Survival Analysis with Interval-Censored Data: A Practical Approach with Examples in R, SAS, and BUGS provides the reader with a practical introduction into the analysis of interval-censored survival times. Although many theoretical developments have appeared in the last fifty years, interval censoring is often ignored in practice. Many are unaware of the impact of inappropriately dealing with interval censoring. In addition, the necessary software is at times difficult to trace. This book fills in the gap between theory and practice.
-Provides an overview of frequentist as well as Bayesian methods.
-Include a focus on practical aspects and applications.
-Extensively illustrates the methods with examples using R, SAS, and BUGS. Full programs are available on a supplementary website.
Kris Bogaerts is project manager at I-BioStat, KU Leuven. He received his PhD in science (statistics) at KU Leuven on the analysis of interval-censored data. He has gained expertise in a great variety of statistical topics with a focus on the design and analysis of clinical trials.
Arnošt Komárek is associate professor of statistics at Charles University, Prague. His subject area of expertise covers mainly survival analysis with the emphasis on interval-censored data and classification based on longitudinal data. He is past chair of the Statistical Modelling Society and editor of Statistical Modelling: An International Journal.
Emmanuel Lesaffre is professor of biostatistics at I-BioStat, KU Leuven. His research interests include Bayesian methods, longitudinal data analysis, statistical modelling, analysis of dental data, interval-censored data, misclassification issues, and clinical trials. He is the founding chair of the Statistical Modelling Society, past-president of the International Society for Clinical Biostatistics, and fellow of ISI and ASA.
Table of Contents
Introduction. Inference for Right-Censored Data. Estimation of the Survival Distribution. Comparison of Two or More Survival Distributions. Proportional Hazard Model. Accelerated Failure Time Model. Bivariate Interval-Censored Data. More Complex Problems. Other Topics in Interval Censoring.
Kris Bogaerts, Arnost Komarek and Emmauel Lesaffre
"The authors succeeded in providing a practical text focused on the application of interval-censored data using various statistical software. Lastly, the authors wrote a text, which appeals to practitioners, because the text anticipates their needs and the foundational concepts and software to execute it."
~ Stephanie A. Besser
"All chapters spend a significant amount of time walking through examples with associated R code and results and do a very nice job explaining the initial CSE framework. Examples expand in complexity as the book progresses. As a biostatistician working in an academic setting, I am quite familiar with simulations used to construct new trials. However, the concept of CSE framework was brand new to me, and I think the strategies outlined in this book could definitely improve my approach to designing trial and analysis plans! This would also facilitate discussions with the clinical study team on how to proceed given our results. I would recommend this book to any clinical trial statistician who is interested in exploring simulations to better understand the implications of selected design and analysis strategies within their trials."
~Emily Dressler, Wake Forest School of Medicine
"To the best of my knowledge, this is the first book to provide a comprehensive treatment of the analysis of interval-censored data using common software such as SAS, R, and BUGS. I expect that applied statisticians and public health researchers with interest in statistical analysis of interval-censored data will find the book very useful. In addition, it seems well suited to be a reference book for a graduate-level survival analysis course. Overall, I enjoyed the presentation of the main idea of the methodology and the discussion of the strengths and limitations of approaches. If I had an opportunity to teach statistical methods for interval-censored data, I would select this book as a required text."
~ Minggen Lu, The