Analysis of Multivariate Survival Data

  • Philip Hougaard

Part of the Statistics for Biology and Health book series (SBH)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Philip Hougaard
    Pages 1-35
  3. Philip Hougaard
    Pages 36-111
  4. Philip Hougaard
    Pages 112-127
  5. Philip Hougaard
    Pages 128-138
  6. Philip Hougaard
    Pages 139-176
  7. Philip Hougaard
    Pages 177-214
  8. Philip Hougaard
    Pages 215-262
  9. Philip Hougaard
    Pages 263-311
  10. Philip Hougaard
    Pages 312-344
  11. Philip Hougaard
    Pages 345-384
  12. Philip Hougaard
    Pages 385-405
  13. Philip Hougaard
    Pages 406-418
  14. Philip Hougaard
    Pages 419-441
  15. Philip Hougaard
    Pages 442-482
  16. Philip Hougaard
    Pages 483-496
  17. Back Matter
    Pages 497-542

About this book


Survival data or more general time-to-event data occur in many areas, including medicine, biology, engineering, economics, and demography, but previously standard methods have requested that all time variables are univariate and independent. This book extends the field by allowing for multivariate times. Applications where such data appear are survival of twins, survival of married couples and families, time to failure of right and left kidney for diabetic patients, life history data with time to outbreak of disease, complications and death, recurrent episodes of diseases and cross-over studies with time responses. As the field is rather new, the concepts and the possible types of data are described in detail and basic aspects of how dependence can appear in such data is discussed. Four different approaches to the analysis of such data are presented. The multi-state models where a life history is described as the subject moving from state to state is the most classical approach. The Markov models make up an important special case, but it is also described how easily more general models are set up and analyzed. Frailty models, which are random effects models for survival data, made a second approach, extending from the most simple shared frailty models, which are considered in detail, to models with more complicated dependence structures over individuals or over time. Marginal modelling has become a popular approach to evaluate the effect of explanatory factors in the presence of dependence, but without having specified a statistical model for the dependence. Finally, the completely non-parametric approach to bivariate censored survival data is described. This book is aimed at investigators who need to analyze multivariate survival data, but due to its focus on the concepts and the modelling aspects, it is also useful for persons interested in such data, but


Multivariate Survival Data STATISTICA Survival Data Time-to-Event Data data analysis linear regression statistical model

Authors and affiliations

  • Philip Hougaard
    • 1
  1. 1.Department of StatisticsNovo Nordisk A/SBagsvaerdDenmark

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag New York, Inc. 2000
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4612-7087-4
  • Online ISBN 978-1-4612-1304-8
  • Series Print ISSN 1431-8776
  • Buy this book on publisher's site
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