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: Regression Methods in Biostatistics
    sub title: Linear, Logistic, Survival, and Repeated Measures Models
  name:
    name part: Vittinghoff, Eric.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Shiboski, Stephen C.
    role:
      role term: author.
  name:
    name part: Glidden, David V.
    role:
      role term: author.
  name:
    name part: McCulloch, Charles E.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2005
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVI, 340 p. online resource.
  abstract: This new book provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics: linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, repeated-measures models for longitudinal and hierarchical outcomes, and generalized linear models for counts and other outcomes. Treating these topics together takes advantage of all they have in common. The authors point out the many-shared elements in the methods they present for selecting, estimating, checking, and interpreting each of these models. They also show that these regression methods deal with confounding, mediation, and interaction of causal effects in essentially the same way. The examples, analyzed using Stata, are drawn from the biomedical context but generalize to other areas of application. While a first course in statistics is assumed, a chapter reviewing basic statistical methods is included. Some advanced topics are covered but the presentation remains intuitive. A brief introduction to regression analysis of complex surveys and notes for further reading are provided. For many students and researchers learning to use these methods, this one book may be all they need to conduct and interpret multipredictor regression analyses. The authors are on the faculty in the Division of Biostatistics, Department of Epidemiology and Biostatistics, University of California, San Francisco, and are authors or co-authors of more than 200 methodological as well as applied papers in the biological and biomedical sciences. The senior author, Charles E. McCulloch, is head of the Division and author of Generalized Linear Mixed Models (2003), Generalized, Linear, and Mixed Models (2000), and Variance Components (1992).
  table of contents: Exploratory and Descriptive Methods -- Basic Statistical Methods -- Linear Regression -- Predictor Selection -- Logistic Regression -- Survival Analysis -- Repeated Measures and Longitudinal Data Analysis -- Generalized Linear Models -- Complex Surveys -- Summary.
  note: by Eric Vittinghoff, Stephen C. Shiboski, David V. Glidden, Charles E. McCulloch.
  subject:
    topic: Mathematics
  subject:
    topic: Public health
  subject:
    topic: Epidemiology
  subject:
    topic: Probabilities
  subject:
    topic: Statistics
  subject:
    topic: Mathematics
  subject:
    topic: Probability Theory and Stochastic Processes
  subject:
    topic: Statistics for Life Sciences, Medicine, Health Sciences
  subject:
    topic: Epidemiology
  subject:
    topic: Public Health
  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: 9780387272559
  identifier: http://dx.doi.org/10.1007/b138825
  record info:
    record creation date: 100301
    record change date: 20151204172609.0
    record identifier: 978-0-387-27255-9