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.
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.
"…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