Evidence map›Paper›PMID 24461459›Full record

ArticleClinical breast cancer2014

Assessing breast cancer risk models in Marin County, a population with high rates of delayed childbirth.

Mark Powell, Farid Jamshidian, Kate Cheyne, Joanne Nititham, Lee Ann Prebil, Rochelle Ereman

Abstract read
In one paragraph

Article in Clinical breast cancer, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 2 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

11 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Review
  5. Review
  6. Article
  7. Comparison of Questionnaire-Based Breast Cancer Prediction Models in the Nurses' Health Study.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2019
    Article
  8. Article
  9. Article
  10. Review
  11. Risk determination and prevention of breast cancer.Breast cancer research : BCR · 2014
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Mark PowellMarin Women's Study, Marin County Health and Human Services, San Rafael, CA. Electronic address: mpowell@marincounty.org.
Farid JamshidianMarin Women's Study, Marin County Health and Human Services, San Rafael, CA.
Kate CheyneMarin Women's Study, Marin County Health and Human Services, San Rafael, CA.
Joanne NitithamMarin Women's Study, Marin County Health and Human Services, San Rafael, CA.
Lee Ann PrebilMarin Women's Study, Marin County Health and Human Services, San Rafael, CA.
Rochelle EremanMarin Women's Study, Marin County Health and Human Services, San Rafael, CA.

Funding

MARIN WOMEN'S STUDY-BREAST CANCER RESEARCHH75DP001730 · DP · COUNTY OF MARIN · PI EREMAN, ROCHELLE R. · 2008 to 2010
$725k
Marin Women's StudyR01DP000217 · DP · MARIN COUNTY DEPT/HEALTH/HUMAN SRVS · PI EREMAN, ROCHELLE R. · 2005 to 2005
–
NCCDPHP CDC HHS 1H75-DP001730-01NCCDPHP CDC HHS 1R01-DP000217-01NCCDPHP CDC HHS 5H75DP001730-03NCCDPHP CDC HHS H75 DP001730NCCDPHP CDC HHS R01 DP000217
6 · The paper itself

Abstract

introductionThis study was designed to compare the Breast Cancer Risk Assessment Tool (BCRAT; Gail), International Breast Intervention Study (IBIS; Tyrer-Cuzick), and BRCAPRO breast cancer risk assessment models using data from the Marin Women's Study, a cohort of women within Marin County, California, with high rates of breast cancer, nulliparity, and delayed childbirth. Existing models have not been well-validated in these high-risk populations.

methodsDiscrimination was assessed using the area under the receiver operating characteristic curve (AUC) and calibration by estimating the ratio of expected-to-observed (E/O) cases. The models were assessed using data from 12,843 participants, of whom 203 had developed cancer during a 5-year period. All tests of statistical significance were 2-sided.

resultsThe IBIS model achieved an AUC of 0.65 (95% confidence interval [CI], 0.61-0.68) compared with 0.62 (95% CI, 0.59-0.66) for BCRAT and 0.60 (95% CI, 0.56-0.63) for BRCAPRO. The corresponding estimated E/O ratios for the models were 1.08 (95% CI, 0.95-1.25), 0.81 (95% CI, 0.71-0.93), and 0.59 (95% CI, 0.52-0.68). In women with age at first birth > 30 years, the AUC for the IBIS, BCRAT, and BRCAPRO models was 0.69 (95% CI, 0.62-0.75), 0.63 (95% CI, 0.56-0.70), and 0.62 (95% CI, 0.56-0.68) and the E/O ratio was 1.15 (95% CI, 0.89-1.47), 0.81 (95% CI, 0.63-1.05), and 0.53 (95% CI, 0.41-0.68), respectively.

conclusionsThe IBIS model was well calibrated for the high-risk Marin mammography population and demonstrated the best calibration of the 3 models in nulliparous women. The IBIS model also achieved the greatest overall discrimination and displayed superior discrimination for women with age at first birth > 30 years.

Indexed as

Reproductive BehaviorAdultAgedArea Under CurveBreast NeoplasmsCaliforniaConfidence IntervalsFemaleHumansMiddle AgedRisk AssessmentRisk FactorsROC CurveBreast cancer risk predictionCalibrationDiscriminationNulliparityRisk factors

Identifiers

PMID24461459
PMCPMC8040293

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.