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: Use of propensity score matching in administrative database research to study rare conditions
  name:
    name part: Inagaki, Kengo
    role:
      role term: creator
    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.
  abstract: This case study will discuss a study assessing an association between symptomatic congenital cytomegalovirus infection and bronchopulmonary dysplasia. It is aimed at illustrating the learning points on the use of propensity score matching in administrative database research for studying rare conditions. Administrative databases are a powerful tool in answering important questions in biomedical research, particularly when the condition of interest is rare. Administrative databases provide advantages of containing a large amount of data and no need for enrolling patients. However, researchers must be familiar with the limitations, because the validity of the study can be undermined without thorough understanding of the properties of the databases. Such limitations may include the lack of detailed clinical information and the intrinsic retrospective nature of administrative databases, among others. The propensity score is the probability of the assignment of a certain variable, conditional on the covariates, and can help mitigate confounding in retrospective studies when used appropriately. Among the commonly used propensity score-based methods, we employed propensity score matching in this study because a comparison between infants with and without congenital cytomegalovirus was expected to be confounded, unless appropriate adjustment was performed. Correct specification of the variables included in the logistic regression model for estimating propensity scores is crucial in ensuring the maximum effectiveness of the method. Although large-scale randomized controlled trials are the most definitive method to assess associations, administrative databases with the use of rigorous statistical methodologies can be an effective research approach, when limitations are well accounted for.
  target audience: specialized
  note: Kengo Inagaki.
  note: Includes bibliographical references and index.
  subject:
    topic: Medicine
    topic: Research
    topic: Data processing
    genre: Case studies
  subject:
    topic: Cytomegalovirus infections
  subject:
    topic: Bronchopulmonary dysplasia
  subject:
    topic: Health services administration
    topic: Data processing
  subject:
    topic: Cytomegalovirus Infections
  subject:
    topic: Bronchopulmonary Dysplasia
  subject:
    topic: Médecine
    topic: Recherche
    topic: Informatique
    genre: Études de cas
  subject:
    topic: Infections à cytomégalovirus
  subject:
    topic: Dysplasie broncho-pulmonaire
  subject:
    topic: Services de santé
    topic: Administration
    topic: Informatique
  subject:
    topic: Bronchopulmonary dysplasia
  subject:
    topic: Cytomegalovirus infections
  subject:
    topic: Health services administration
    topic: Data processing
  subject:
    topic: Medicine
    topic: Research
    topic: Data processing
  classification: R853.D37 I53 2020
  classification: 610.285
  related item:
    title info:
      title: SAGE Research Methods Cases: Medicine and Health
  identifier: 9781529723571
  identifier: 1529723574
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: SGPBL
    record creation date: 200123
    record change date: 20240923170424.7
    record identifier: in00005495673
    language of cataloging:
      language term: eng