Bayesian Survival Analysis

  • Joseph G. Ibrahim
  • Ming-Hui Chen
  • Debajyoti Sinha

Part of the Springer Series in Statistics book series (SSS)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Joseph G. Ibrahim, Ming-Hui Chen, Debajyoti Sinha
    Pages 1-29
  3. Joseph G. Ibrahim, Ming-Hui Chen, Debajyoti Sinha
    Pages 30-46
  4. Joseph G. Ibrahim, Ming-Hui Chen, Debajyoti Sinha
    Pages 47-99
  5. Joseph G. Ibrahim, Ming-Hui Chen, Debajyoti Sinha
    Pages 100-154
  6. Joseph G. Ibrahim, Ming-Hui Chen, Debajyoti Sinha
    Pages 155-207
  7. Joseph G. Ibrahim, Ming-Hui Chen, Debajyoti Sinha
    Pages 208-261
  8. Joseph G. Ibrahim, Ming-Hui Chen, Debajyoti Sinha
    Pages 262-289
  9. Joseph G. Ibrahim, Ming-Hui Chen, Debajyoti Sinha
    Pages 290-319
  10. Joseph G. Ibrahim, Ming-Hui Chen, Debajyoti Sinha
    Pages 320-351
  11. Joseph G. Ibrahim, Ming-Hui Chen, Debajyoti Sinha
    Pages 352-435
  12. Back Matter
    Pages 436-481

About this book

Introduction

Survival analysis arises in many fields of study including medicine, biology, engineering, public health, epidemiology, and economics. This book provides a comprehensive treatment of Bayesian survival analysis.
Several topics are addressed, including parametric models, semiparametric models based on prior processes, proportional and non-proportional hazards models, frailty models, cure rate models, model selection and comparison, joint models for longitudinal and survival data, models with time varying covariates, missing covariate data, design and monitoring of clinical trials, accelerated failure time models, models for mulitivariate survival data, and special types of hierarchial survival models. Also various censoring schemes are examined including right and interval censored data. Several additional topics are discussed, including noninformative and informative prior specificiations, computing posterior qualities of interest, Bayesian hypothesis testing, variable selection, model selection with nonnested models, model checking techniques using Bayesian diagnostic methods, and Markov chain Monte Carlo (MCMC) algorithms for sampling from the posteiror and predictive distributions.
The book presents a balance between theory and applications, and for each class of models discussed, detailed examples and analyses from case studies are presented whenever possible. The applications are all essentially from the health sciences, including cancer, AIDS, and the environment. The book is intended as a graduate textbook or a reference book for a one semester course at the advanced masters or Ph.D. level. This book would be most suitable for second or third year graduate students in statistics or biostatistics. It would also serve as a useful reference book for applied or theoretical researchers as well as practitioners.

Keywords

Bayesian Survival Analysis Censoring Survival analysis biology currdeb statistics

Authors and affiliations

  • Joseph G. Ibrahim
    • 1
  • Ming-Hui Chen
    • 2
  • Debajyoti Sinha
    • 3
  1. 1.Department of BiostatisticsHarvard School of Public Health and Dana-Farber Cancer InstituteBostonUSA
  2. 2.Department of Mathematical SciencesWorcester Polytechnic InstituteWorcesterUSA
  3. 3.Department of Biometry and EpidemiologyMedical Universtiy of South CarolinaCharlestonUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4757-3447-8
  • Copyright Information Springer-Verlag New York 2001
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4419-2933-4
  • Online ISBN 978-1-4757-3447-8
  • Series Print ISSN 0172-7397
  • About this book