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

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ISBN 9781466504257
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  • Provides up-to-date methodologies for interval-censored time-to-event data
  • Offers easy access to computational methods and R software packages
  • Presents data from actual clinical trials and biomedical research, including breast cancer and HIV data sets
  • Formulates statistical analysis plans associated with real-world clinical data


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.

Table of Contents

Introduction and Overview
Overview of Recent Developments for Interval-Censored Data, Jianguo Sun and Junlong Li
A Review of Various Models for Interval-Censored Data, Qiqing Yu and Yuting Hsu

Current Status Data in the Twenty-First Century, Moulinath Banerjee
Regression Analysis for Current Status Data, Bin Zhang
Statistical Analysis of Dependent Current Status Data, Yang-Jin Kim, Jinheum Kim, Chung Mo Nam, and Youn Nam Kim
Bayesian Semiparametric Regression Analysis of Interval-Censored Data with Monotone Splines, Lianming Wang, Xiaoyan (Iris) Lin, and Bo Cai
Bayesian Inference of Interval-Censored Survival Data, Xiaojing Wang, Arijit Sinha, Jun Yan, and Ming-Hui Chen
Targeted Minimum Loss-Based Estimation of a Causal Effect Using Interval-Censored Time-to-Event Data, Marco Carone, Maya Petersen, and Mark J. van der Laan
Consistent Variance Estimation in Interval-Censored Data, Jian Huang, Ying Zhang, and Lei Hua

Applications and Related Software
Bias Assessment in Progression-Free Survival Analysis, Chen Hu, Kalyanee Viraswami-Appanna, and Bharani Dharan
Bias and Its Remedy in Interval-Censored Time-to-Event Applications, Ding-Geng (Din) Chen, Lili Yu, Karl E. Peace, and Jianguo Sun
Adaptive Decision Making Based on Interval-Censored Data in a Clinical Trial to Optimize Rapid Treatment of Stroke, Peter F. Thall, Hoang Q. Nguyen, and Aniko Szabo
Practical Issues on Using Weighted Logrank Tests, Michael P. Fay and Sally A. Hunsberger
glrt — New R Package for Analyzing Interval-Censored Survival Data, Qiang Zhao


A Bibliography appears at the end of each chapter.

Author Bio(s)