Interval-Censored Time-to-Event Data

Ding-Geng (Din) Chen, Ph.D., is a professor at the University of Rochester Medical Center. Dr. Chen is also a senior biostatistics consultant for biopharmaceutical companies and government agencies. He is a member of the ASA, chair-elect for the STAT section of the American Public Health Association, and an associate editor of the Journal of Statistical Computation and Simulation. He has authored/co-authored more than 80 journal publications on biostatistical methodologies and applications and co-authored two books with Dr. Peace, Clinical Trial Methodology and Clinical Trial Data Analysis Using R.Jianguo (Tony) Sun, Ph.D., is a professor of statistics at the University of Missouri. He has worked on failure time analysis for over 20 years and published many papers on failure time analysis, chemometrics, longitudinal data analysis, and panel count data analysis. He also authored the book, Statistical Analysis of Interval-censored Failure Time Data. Karl E. Peace, Ph.D., is the Georgia Cancer Coalition Distinguished Cancer Scholar, senior research scientist, and professor of biostatistics in the Jiann-Ping Hsu College of Public Health at Georgia Southern University, where he is the founding director of the Center for Biostatistics. A fellow of the ASA, he has received numerous honors, including citations from the Georgia and U.S. Congressional Houses for contributions to education and public health, the Hall of Fame Alumni Award from Georgia Southern University System’s Board of Regents, and the ASA Award for Statistical Contributions for the Betterment of Society. Dr. Peace has authored/edited ten books and authored/co-authored over 200 articles. His primary research interests include drug research and development, clinical trial methodology, time-to-event methodology, and public health applications of biostatistics.

"…a single-volume overview of the latest developments in time-to-event interval censoring methods, along with their applications."—ISCB News, December 2015"… a nice summary of interval-censored survival data analysis and, in addition, describes some recent advances in this area. It is suitable for researchers and postgraduate students who require skills in survival analysis with interval censored data, and furthermore can be used as supplementary reading to some existing books and book chapters on interval censoring."—Australian & New Zealand Journal of Statistics, 2015

• Introduction and Overview: Overview of Recent Developments for Interval-Censored Data. A Review of Various Models for Interval-Censored Data. Methodology: Current Status Data in the Twenty-First Century. Regression Analysis for Current Status Data. Statistical Analysis of Dependent Current Status Data. Bayesian Semiparametric Regression Analysis of Interval-Censored Data with Monotone Splines. Bayesian Inference of Interval-Censored Survival Data. Targeted Minimum Loss-Based Estimation of a Causal Effect Using Interval-Censored Time-to-Event Data. Consistent Variance Estimation in Interval-Censored Data. Applications and Related Software: Bias Assessment in Progression-Free Survival Analysis. Bias and Its Remedy in Interval-Censored Time-to-Event Applications. Adaptive Decision Making Based on Interval-Censored Data in a Clinical Trial to Optimize Rapid Treatment of Stroke. Practical Issues on Using Weighted Logrank Tests. glrt — New R Package for Analyzing Interval-Censored Survival Data. Index.