Selection bias and competing risk.

The article focuses on the advancements in understanding selection bias, particularly in the context of genetic studies and competing risks related to age at death. It highlights how selection bias can arise when individuals who have died before recruitment are excluded from studies, potentially lea...

Descripción completa

Detalles Bibliográficos
Publicado en:American Journal of Epidemiology Vol. 194; no. 11; pp. 3396 - 3398
Autor principal: Schooling, C Mary
Formato: letter tables/charts Journal Article
Publicado: Oxford University Press / USA Nov2025
Acceso en línea:Ver este registro en EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=189501682&site=ehost-live
header:
  @attributes:
    shortDbName: ccm
    uiTerm: 189501682
    longDbName: CINAHL Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    dissinfo:
    jinfo:
      jid:
        00029262
        1X1
      jtl: American Journal of Epidemiology
      issn: 00029262
      maglogo: N
    pubinfo:
      dt: Nov2025
      vid: 194
      iid: 11
      pid: 622
      pub: Oxford University Press / USA
    artinfo:
      ui:
        189501682
        189501682
        189501682
        10.1093/aje/kwaf174
        189501682
      ppf: 3396
      ppct: 2
      formats:
      tig:
        atl: Selection bias and competing risk.
      aug:
        au: Schooling, C Mary
        affil: Li Ka Shing Faculty of Medicine, School of Public Health, CUNY Graduate School of Public Health and Health Policy, The University of Hong Kong, Pokfulam, Hong Kong
      sug:
        subj:
          Selection Bias
          Epidemiological Research
          Research Subject Recruitment
          Death
          Age Factors
          Mendelian Randomization
      ab: The article focuses on the advancements in understanding selection bias, particularly in the context of genetic studies and competing risks related to age at death. It highlights how selection bias can arise when individuals who have died before recruitment are excluded from studies, potentially leading to statistical errors and affecting causal inferences. The article also discusses the significance of disease-specific age at death patterns, illustrated with data from the United States in 2019, to better interpret studies involving long-term exposures and interventions. It emphasizes the need for precise information to mitigate biases and contextualize findings related to survival and disease outcomes.
      pubtype: Academic Journal
      doctype:
        letter
        tables/charts
        Journal Article
      ougenre: Article
    language: English
    refInfo:
    holdings:
      @attributes:
        islocal: N