Design Characteristics Influence Performance of Clinical Prediction Rules in Validation: A Meta-Epidemiological Study
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BACKGROUND: Many new clinical prediction rules are derived and validated. But the design and reporting quality of clinical prediction research has been less than optimal. We aimed to assess whether design characteristics of validation studies were associated with the overestimation of clinical prediction rules' performance. We also aimed to evaluate whether validation studies clearly reported important methodological characteristics. METHODS: Electronic databases were searched for systematic reviews of clinical prediction rule studies published between 2006 and 2010. Data were extracted from the eligible validation studies included in the systematic reviews. A meta-analytic meta-epidemiological approach was used to assess the influence of design characteristics on predictive performance. From each validation study, it was assessed whether 7 design and 7 reporting characteristics were properly described. RESULTS: A total of 287 validation studies of clinical prediction rule were collected from 15 systematic reviews (31 meta-analyses). Validation studies using case-control design produced a summary diagnostic odds ratio (DOR) 2.2 times (95% CI: 1.2-4.3) larger than validation studies using cohort design and unclear design. When differential verification was used, the summary DOR was overestimated by twofold (95% CI: 1.2 -3.1) compared to complete, partial and unclear verification. The summary RDOR of validation studies with inadequate sample size was 1.9 (95% CI: 1.2 -3.1) compared to studies with adequate sample size. Study site, reliability, and clinical prediction rule was adequately described in 10.1%, 9.4%, and 7.0% of validation studies respectively. CONCLUSION: Validation studies with design shortcomings may overestimate the performance of clinical prediction rules. The quality of reporting among studies validating clinical prediction rules needs to be improved.
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Common sources of heterogeneity are differences in study characteristics, differences in study quality, or differences in baseline risk across the validation studies.
“Common sources of heterogeneity are differences in study characteristics,62 differences in study quality, or differences in baseline risk across the validation studies.9,10”
From A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes (2019) · cites this paper as [62]
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- Authors
- Ban, J.-W., Emparanza, J. I., Urreta, I., & Burls, A.
- Affiliations
- University of Oxford (GB)
- Donostiako Unibertsitate Ospitalea (ES)
- City, University of London (GB)
- Corresponding author
- Ban, Jong-Wook
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2016-01-05
- Volume
- 11
- Issue
- 1
- Pages
- e0145779
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4701404
- OpenAlex
- W2223444119
- Semantic Scholar
- 80f410a0b8e4c3907dd6a88df306d7fde70b6e6f
- Cited by
- 18
- References
- 62
- Citations by year
- 2026: 2 · 2025: 2 · 2022: 3 · 2021: 3 · 2020: 2 · 2019: 2 · 2018: 3 · 2017: 1
- FWCI
- 0.45
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- City Research Online (City University London) (acceptedVersion)
- City Research Online (City University London) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Keywords
- clinical prediction rules, diagnostic odds ratio, case-control study, sample size adequacy, reporting quality, meta-epidemiology
- MeSH terms
- Decision Support Techniques, Humans, Research Design, Reproducibility of Results, Odds Ratio, Epidemiologic Studies, Sample Size, Biomedical Research, Validation Studies as Topic, Research Report
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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