rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Survival and Event History Analysis
    sub title: A Process Point of View
  name:
    name part: Aalen, Odd O.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Borgan, Ørnulf.
    role:
      role term: author.
  name:
    name part: Gjessing, Håkon K.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2008
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVIII, 540 p. online resource.
  abstract: Time-to-event data are ubiquitous in fields such as medicine, biology, demography, sociology, economics and reliability theory. Recently, a need to analyze more complex event histories has emerged. Examples are individuals that move among several states, frailty that makes some units fail before others, internal time-dependent covariates, and the estimation of causal effects from observational data. The aim of this book is to bridge the gap between standard textbook models and a range of models where the dynamic structure of the data manifests itself fully. The common denominator of such models is stochastic processes. The authors show how counting processes, martingales, and stochastic integrals fit very nicely with censored data. Beginning with standard analyses such as Kaplan-Meier plots and Cox regression, the presentation progresses to the additive hazard model and recurrent event data. Stochastic processes are also used as natural models for individual frailty; they allow sensible interpretations of a number of surprising artifacts seen in population data. The stochastic process framework is naturally connected to causality. The authors show how dynamic path analyses can incorporate many modern causality ideas in a framework that takes the time aspect seriously. To make the material accessible to the reader, a large number of practical examples, mainly from medicine, are developed in detail. Stochastic processes are introduced in an intuitive and non-technical manner. The book is aimed at investigators who use event history methods and want a better understanding of the statistical concepts. It is suitable as a textbook for graduate courses in statistics and biostatistics. Odd O. Aalen is professor of medical statistics at the University of Oslo, Norway. His Ph.D. from the University of California, Berkeley in 1975 introduced counting processes and martingales in event history analysis. He has also contributed to numerous other areas of event history analysis,…
  table of contents: An introduction to survival and event history analysis -- Stochastic processes in event history analysis -- Nonparametric analysis of survival and event history data -- Regression models -- Parametric counting process models -- Unobserved heterogeneity: The odd effects of frailty -- Multivariate frailty models -- Marginal and dynamic models for recurrent events and clustered survival data -- Causality -- First passage time models: Understanding the shape of the hazard rate -- Diffusion and L#x00E9;vy process models for dynamic frailty.
  note: by Odd O. Aalen, Ørnulf Borgan, Håkon K. Gjessing.
  subject:
    topic: Mathematics
  subject:
    topic: Epidemiology
  subject:
    topic: Probabilities
  subject:
    topic: Statistics
  subject:
    topic: Quality control
  subject:
    topic: Reliability
  subject:
    topic: Industrial safety
  subject:
    topic: Econometrics
  subject:
    topic: Mathematics
  subject:
    topic: Probability Theory and Stochastic Processes
  subject:
    topic: Statistics for Life Sciences, Medicine, Health Sciences
  subject:
    topic: Epidemiology
  subject:
    topic: Econometrics
  subject:
    topic: Quality Control, Reliability, Safety and Risk
  classification: QA273.A1-274.9
  classification: QA274-274.9
  classification: 519.2
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Statistics for Biology and Health
  identifier: 9780387685601
  identifier: http://dx.doi.org/10.1007/978-0-387-68560-1
  record info:
    record creation date: 100301
    record change date: 20151204160941.0
    record identifier: 978-0-387-68560-1