The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Assessing Bias in Studies of Prognostic Factors
Abstract
Previous work has identified 6 important areas to consider when evaluating validity and bias in studies of prognostic factors: participation, attrition, prognostic factor measurement, confounding measurement and account, outcome measurement, and analysis and reporting. This article describes the Quality In Prognosis Studies tool, which includes questions related to these areas that can inform judgments of risk of bias in prognostic research.A working group comprising epidemiologists, statisticians, and clinicians developed the tool as they considered prognosis studies of low back pain. Forty-three groups reviewing studies addressing prognosis in other topic areas used the tool and provided feedback. Most reviewers (74%) reported that reaching consensus on judgments was easy. Median completion time per study was 20 minutes; interrater agreement (κ statistic) reported by 9 review teams varied from 0.56 to 0.82 (median, 0.75). Some reviewers reported challenges making judgments across prompting items, which were addressed by providing comprehensive guidance and examples. The refined Quality In Prognosis Studies tool may be useful to assess the risk of bias in studies of prognostic factors.
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“Currently there is little information available to guide the development of a well-defined review question however the Quality in Prognosis Studies (QUIPS) tool [34] and the Checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies (CHARMS Checklist) [38] have been developed to assist in this process (see Table 1).”
From What kind of systematic review should I conduct? A proposed typology and guidance for systematic reviewers in the medical and health sciences (2018) · cites this paper as [34]
In the citing paper
“Prognostic research is of high value as it provides clinicians and patients with information regarding the course of a disease and potential outcomes, in addition to potentially providing useful information to deliver targeted therapy relating to specific prognostic factors [20, 34, 35].”
From What kind of systematic review should I conduct? A proposed typology and guidance for systematic reviewers in the medical and health sciences (2018) · cites this paper as [34]
Citations
Cites
The Cochrane Collaboration's tool for assessing risk of bias in randomised trials
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QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies
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Cited by
Sorted by citations
Dementia prevention, intervention, and care
Livingston, G., Sommerlad, A., Orgeta, V., Costafreda, S. G., Huntley, J., Ames, D., Ballard, C., Banerjee, S., Burns, A., Cohen-Mansfield, J., et al. (2017) · The Lancet · doi:10.1016/s0140-6736(17)31363-6 · Cited by 6,072
PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies
Wolff, R. F., Moons, K. G. M., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., Mallett, S., & for the PROBAST Group† (2019) · Annals of Internal Medicine · doi:10.7326/m18-1376 · Cited by 2,984
Moons, K. G. M., de Groot, J. A. H., Bouwmeester, W., Vergouwe, Y., Mallett, S., Altman, D. G., Reitsma, J. B., & Collins, G. S. (2014) · PLoS Medicine · doi:10.1371/journal.pmed.1001744 · Cited by 2,018
Moons, K. G. M., Wolff, R. F., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., & Mallett, S. (2019) · Annals of Internal Medicine · doi:10.7326/m18-1377 · Cited by 1,669
Ma, L.-L., Wang, Y.-Y., Yang, Z.-H., Huang, D., Weng, H., & Zeng, X.-T. (2020) · Military Medical Research · doi:10.1186/s40779-020-00238-8 · Cited by 1,648
Metadata
- Authors
- Hayden, J. A., van der Windt, D. A., Cartwright, J. L., Côté, P., & Bombardier, C.
- Affiliations
- Dalhousie University (CA)
- From Dalhousie University, Halifax, Nova Scotia, Canada; Arthritis Research UK Primary Care Centre, Primary Care and Health Sciences, Keele University, Staffordshire, United Kingdom; University of Ontario Institute of Technology, Oshawa, Ontario, Canada; and University of Toronto and Institute for Work & Health, Toronto, Ontario, Canada.
- Venue
- Annals of Internal Medicine
- Publisher
- American College of Physicians
- Published
- 2013-02-19
- Volume
- 158
- Issue
- 4
- Pages
- 280-286
- ISSN
- 0003-4819 (ISSN-L)
- 1539-3704
- OpenAlex
- W2082977560
- Cited by
- 3,287
- References
- 2
- Citations by year
- 2026: 408 · 2025: 325 · 2024: 330 · 2023: 342 · 2022: 381 · 2021: 418 · 2020: 335 · 2019: 212 · 2018: 171 · 2017: 145 · 2016: 106 · 2015: 73 · 2014: 33 · 2013: 6
- FWCI
- 80.55
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Annals of Internal Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Sociology and Political Science › Delphi Technique in Research
- Keywords
- risk of bias, prognostic factors, bias assessment, interobserver agreement, research validity, attrition, confounding, outcome measurement, reporting quality
- MeSH terms
- Humans, Prognosis, Research Design, Review Literature as Topic, Bias, Low Back Pain
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