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: Making the most of your data
    sub title: using an alternative statistical methodology to multilevel modeling to investigate hospital effects on acute hospital length of stay following stroke when number of hospitals is small
  name:
    name part: Tørnes, Michelle
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Myint, Phyo Kyaw
    role:
      role term: author.
  name:
    name part: McLernon, David J.
    role:
      role term: author.
  type of resource: text
  genre: bibliography
  genre: Case studies.
  origin info:
    place:
      place term: enk
    date issued: 2020
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource : illustrations.
  abstract: Recent research in stroke has shown that differences in hospital characteristics such as staffing levels and facility provision partly explain variations in mortality rates seen between hospitals. It is important to determine whether this also applies to other important stroke-related outcomes. If it can be shown that hospital characteristics play a role in determining disparities in patient outcomes after stroke, policy makers and service commissioners can target modifiable hospital characteristics to improve outcomes and increase equity in care. The influence of hospital characteristics on patient outcomes is regarded as contextual effects. Medical researchers interested in investigating contextual effects usually employ multilevel modeling techniques as it overcomes the limitations of traditional single-level regression models by modeling relationships independently at different levels. In this case study, we demonstrate that the use of multilevel modeling when the number of hospitals sampled is small leads to vastly misleading results. We show how employing a much more conservative, robust approach which considers hospital as a fixed-effect should be favored. Such an approach explores the relationship between the outcome of interest (which in this example is length of stay) and hospital characteristics in a descriptive manner. By studying this case study, researchers should have an understanding as to why they should avoid the popularized multilevel modeling method when the number of hospitals is small and consider employing this alternative approach that makes the best use of the data in the most robust way to overcome such limitations.
  target audience: specialized
  note: Michelle Tørnes, Phyo Kyaw Myint, David J. McLernon.
  note: Includes bibliographical references and index.
  subject:
    topic: Hospital utilization
    topic: Length of stay
    topic: Research
    topic: Methodology
    genre: Case studies
  subject:
    topic: Cerebrovascular disease
    topic: Patients
    topic: Hospital care
    genre: Case studies
  subject:
    topic: Soins hospitaliers
    topic: Durée
    topic: Recherche
    topic: Méthodologie
    genre: Études de cas
  subject:
    topic: Apoplectiques
    topic: Soins hospitaliers
    genre: Études de cas
  classification: RA409 .T67 2020
  classification: 610.727
  related item:
    title info:
      title: SAGE Research Methods Cases: Medicine and Health
  identifier: 9781529719994
  identifier: 1529719992
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: SGPBL
    record creation date: 200123
    record change date: 20240923170426.4
    record identifier: in00005495740
    language of cataloging:
      language term: eng