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. Top researchers from academia, biopharmaceutical industries, and government agencies show how up-to-date statistical methods are used in biopharmaceutical and public health applications. The book presents data from actual clinical trials and biomedical research, including breast cancer and HIV data sets. It also offers easy access to computational methods and R software packages.

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.