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

Ding-Geng (Din) Chen, Jianguo Sun, Karl E. Peace

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July 19, 2012 by Chapman and Hall/CRC
Professional - 433 Pages - 15 B/W Illustrations
ISBN 9781466504257 - CAT# K14515
Series: Chapman & Hall/CRC Biostatistics Series

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Features

  • 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

Summary

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.