Interval-Censored Time-to-Event Data: Methods and Applications

Interval-Censored Time-to-Event Data: Methods and Applications collects the most recent techniques, models, and computational tools for interval-censored time-to-event data. Top biostatisticians from academia, biopharmaceutical industries, and government agencies discuss how these advances are impacting clinical trials and biomedical research.

Divided into three parts, the book begins with an overview of interval-censored data modeling, including nonparametric estimation, survival functions, regression analysis, multivariate data analysis, competing risks analysis, and other models for interval-censored data. The next part presents interval-censored methods for current status data, Bayesian semiparametric regression analysis of interval-censored data with monotone splines, Bayesian inferential models for interval-censored data, an estimator for identifying causal effect of treatment, and consistent variance estimation for interval-censored data. In the final part, the contributors use Monte Carlo simulation to assess biases in progression-free survival analysis as well as correct bias in interval-censored time-to-event applications. They also present adaptive decision making methods to optimize the rapid treatment of stroke, explore practical issues in using weighted logrank tests, and describe how to use two R packages.

A practical guide for biomedical researchers, clinicians, biostatisticians, and graduate students in biostatistics, this volume covers the latest developments in the analysis and modeling of interval-censored time-to-event data. It shows how up-to-date statistical methods are used in biopharmaceutical and public health applications.

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.