Evidence map›Paper›PMID 29381683›Full record

ArticleBritish journal of cancer2018

External validation of risk prediction models for incident colorectal cancer using UK Biobank.

J A Usher-Smith, A Harshfield, C L Saunders, S J Sharp, J Emery, F M Walter, K Muir, S J Griffin

Erratum issuedAbstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in British journal of cancer, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 38 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 5 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

38 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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  5. Risk Prediction Models for Colorectal Cancer Incorporating Common Genetic Variants: A Systematic Review.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2019
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

J A Usher-SmithThe Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB2 0SR, UK.
A HarshfieldThe Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB2 0SR, UK.
C L SaundersThe Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB2 0SR, UK.
S J SharpMRC Epidemiology Unit, University of Cambridge, Institute of Metabolic Science, Cambridge CB2 0QQ, UK.
J EmeryDepartment of General Practice, Centre for Cancer Research, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Victorian Comprehensive Cancer Centre, Melbourne, VIC 3010, Australia.
F M WalterThe Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB2 0SR, UK.
K MuirInstitute of Population Health, University of Manchester, Manchester M13 9PL, UK.
S J GriffinThe Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB2 0SR, UK.

Funding

Department of HealthMedical Research Council MC_QA137853Medical Research Council MC_ UU_12015/1Medical Research Council MC_UU_12015/1Medical Research Council MC_UU_12015/4
6 · The paper itself

Abstract

backgroundThis study aimed to compare and externally validate risk scores developed to predict incident colorectal cancer (CRC) that include variables routinely available or easily obtainable via self-completed questionnaire.

methodsExternal validation of fourteen risk models from a previous systematic review in 373 112 men and women within the UK Biobank cohort with 5-year follow-up, no prior history of CRC and data for incidence of CRC through linkage to national cancer registries.

resultsThere were 1719 (0.46%) cases of incident CRC. The performance of the risk models varied substantially. In men, the QCancer10 model and models by Tao, Driver and Ma all had an area under the receiver operating characteristic curve (AUC) between 0.67 and 0.70. Discrimination was lower in women: the QCancer10, Wells, Tao, Guesmi and Ma models were the best performing with AUCs between 0.63 and 0.66. Assessment of calibration was possible for six models in men and women. All would require country-specific recalibration if estimates of absolute risks were to be given to individuals.

conclusionsSeveral risk models based on easily obtainable data have relatively good discrimination in a UK population. Modelling studies are now required to estimate the potential health benefits and cost-effectiveness of implementing stratified risk-based CRC screening.

Indexed as

AgedBiological Specimen BanksColorectal NeoplasmsEarly Detection of CancerFemaleHumansIncidenceMaleMiddle AgedModels, TheoreticalRegistriesRisk AssessmentSurveys and QuestionnairesUnited Kingdom

Identifiers

PMID29381683
PMCPMC5846069

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.