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Cited by 1,064Statistical Methods in EpidemiologyMeta-analysis and systematic reviewsAdvanced Causal Inference Techniques

Bias: Table 1

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Delgado-Rodríguez, M, Llorca, J·2004·Journal of Epidemiology and Community Health
DOI 10.1136/jech.2003.008466
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Abstract

The concept of bias is the lack of internal validity or incorrect assessment of the association between an exposure and an effect in the target population in which the statistic estimated has an expectation that does not equal the true value. Biases can be classified by the research stage in which they occur or by the direction of change in a estimate. The most important biases are those produced in the definition and selection of the study population, data collection, and the association between different determinants of an effect in the population. A definition of the most common biases occurring in these stages is given.

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  • Time to diagnosis was approximately 1 year longer in the never-treated population, which could indicate a healthy survivor effect.

    “This is especially important since time to diagnosis was ∼1 year longer in the never-treated population, which could indicate a “healthy survivor effect” [28].”

    From Survival and course of lung function in the presence or absence of antifibrotic treatment in patients with idiopathic pulmonary fibrosis: long-term results of the INSIGHTS-IPF registry (2020) · cites this paper as [28]

    Section 1.1 describes selective survival and survivor treatment selection bias, supporting the possibility of a survivor-related effect. It does not report a never-treated population or an approximately one-year difference in time to diagnosis; searches for those details found no matching passage. Page numbers were not available in our copy. (LLM, conf=0.85)

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  • A recent simulation study by Nelson et al. showed that first-generation methylation ageing clocks do worse than random chance at identifying causal ageing loci due to cohort selection effects in cross-sectional ageing cohorts.

    “A recent simulation study by Nelson et al.168 bolsters the rationale for training these composite models, showing that first-generation methylation ageing clocks do worse than random chance at identifying causal ageing loci due to cohort selection effects that occur in cross-sectional ageing cohorts169,170.”

    From Measuring biological age using omics data (2022) · cites this paper as [169]

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Research works printed but not linked: 5

  1. [12]12. Gail M. Competing risks. In: Kotz S, Johnson NL, eds. Encyclopedia of statistical sciences. Vol 2. New York: Wiley, 1982:75-81.
  2. [15]15. Delgado-Rodrı ´guez, Llorca www.jech.com on August 18, 2023 by guest. Protected by copyright. http://jech.bmj.com/ J Epidemiol Community Health: first published as 10.1136/jech.2003.008466 on 13 July 2004. Downloaded from
  3. [46]46. Morgenstern H. Ecologic study. In: Gail MH, Benichou J, eds. Encyclopedia of epidemiologic methods. New York: Wiley, 2000:315-36.
  4. [48]48. Bland JM, Altman DG. Some examples of regression towards the mean. BMJ 1994;309:780.
  5. [51]51. Herna ´ndez Aguado I, Ruiz Atienza L, Ferna ´ndez Garcı ´a I, et al. Evaluacio ´n de la variabilidad del diagno ´stico ra ´pido de infeccio ´n por Chlamydia tracomatis mediante inmunofluorescencia directa. Med Clı ´n (Barc) 1990;94:9-11.

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Authors
Delgado-Rodríguez, M., & Llorca, J.
Affiliations
Universidad de Cantabria (ES)
ORCID
Llorca, J
Venue
Journal of Epidemiology and Community Health
Publisher
BMJ
Published
2004-07-13
Volume
58
Issue
8
Pages
635-641
ISSN
  • 0141-7681 (ISSN-L)
  • 0143-005X
  • 1470-2738
DOI
10.1136/jech.2003.008466
OpenAlex
W4248217291
Cited by
1,064
References
71
Citations by year
2026: 44 · 2025: 59 · 2024: 92 · 2023: 95 · 2022: 92 · 2021: 84 · 2020: 85 · 2019: 57 · 2018: 54 · 2017: 63 · 2016: 75 · 2015: 68 · 2014: 44 · 2013: 24 · 2012: 14
FWCI
13.17
Type
journal article
Language
en
Open access
Locations
  • Journal of Epidemiology & Community Health (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Keywords
bias, internal validity, population selection, data collection errors, confounding, measurement error
MeSH terms
Data Collection, Epidemiologic Methods, Humans, Terminology as Topic, Research Design, Reproducibility of Results, Bias, Selection Bias, Confounding Factors, Epidemiologic

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