Evidence map›Paper›PMID 27770283›Full record

ArticleBreast cancer research and treatment2016

Accounting for individualized competing mortality risks in estimating postmenopausal breast cancer risk.

Mara A Schonberg, Vicky W Li, A Heather Eliassen, Roger B Davis, Andrea Z LaCroix, Ellen P McCarthy, Bernard A Rosner, Rowan T Chlebowski, Susan E Hankinson, Edward R Marcantonio and 1 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Breast cancer research and treatment, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 2 pooled it
0.5field-weighted citation impact, top 29% of its field
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

9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 13 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Article
  5. Article
  6. Article
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  8. Review
  9. 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

11 authors at 4 institutions in 1 country.

Mara A SchonbergDivision of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA. mschonbe@bidmc.harvard.edu.ORCID 0000-0001-5162-5922
Vicky W LiDivision of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA.
A Heather EliassenDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Roger B DavisDivision of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Andrea Z LaCroixDivision of Epidemiology, Family Medicine and Public Health, University of California San Diego, La Jolla, CA, USA.
Ellen P McCarthyDivision of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Bernard A RosnerDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Rowan T ChlebowskiLos Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA.
Susan E HankinsonDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Edward R MarcantonioDivision of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Long H NgoDivision of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Beth Israel Deaconess Medical Center · USBrigham and Women's Hospital · USThe Lundquist Institute · USUniversity of California San Diego · US

Funding

Statistical MethodsP01CA087969 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI ROSNER, BERNARD A · 2000 to 2019
$77.8M
Long Term Multidisciplinary Study of Cancer in Women: The Nurses Health StudyUM1CA186107 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI ELIASSEN, A. HEATHER, STAMPFER, MEIR · 2014 to 2023
$22.3M
Aging Among the Homeless: Social Isolation, Function and Institutional CareR01AG041860 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Margot B Kushel · 2012 to 2026
$9.3M
Breast Cancer Risk Factors Among Women Aged 75 and OlderR01AG041869 · NIA · BETH ISRAEL DEACONESS MEDICAL CENTER · PI SCHONBERG, MARA A · 2013 to 2015
$1.4M
NCI NIH HHS P01 CA087969NCI NIH HHS UM1 CA186107NHLBI NIH HHS HHSN268201100001INHLBI NIH HHS HHSN268201100002INHLBI NIH HHS HHSN268201100003INHLBI NIH HHS HHSN268201100004INHLBI NIH HHS HHSN268201100046CNIA NIH HHS HHSN271201100004CNIA NIH HHS R01 AG041860NIA NIH HHS R01 AG041869WHI NIH HHS HHSN268201100001CWHI NIH HHS HHSN268201100002CWHI NIH HHS HHSN268201100003CWHI NIH HHS HHSN268201100004C
6 · The paper itself

Abstract

purposeAccurate risk assessment is necessary for decision-making around breast cancer prevention. We aimed to develop a breast cancer prediction model for postmenopausal women that would take into account their individualized competing risk of non-breast cancer death.

methodsWe included 73,066 women who completed the 2004 Nurses' Health Study (NHS) questionnaire (all ≥57 years) and followed participants until May 2014. We considered 17 breast cancer risk factors (health behaviors, demographics, family history, reproductive factors) and 7 risk factors for non-breast cancer death (comorbidities, functional dependency) and mammography use. We used competing risk regression to identify factors independently associated with breast cancer. We validated the final model by examining calibration (expected-to-observed ratio of breast cancer incidence, E/O) and discrimination (c-statistic) using 74,887 subjects from the Women's Health Initiative Extension Study (WHI-ES; all were ≥55 years and followed for 5 years).

resultsWithin 5 years, 1.8 % of NHS participants were diagnosed with breast cancer (vs. 2.0 % in WHI-ES, p = 0.02), and 6.6 % experienced non-breast cancer death (vs. 5.2 % in WHI-ES, p < 0.001). Using a model selection procedure which incorporated the Akaike Information Criterion, c-statistic, statistical significance, and clinical judgement, our final model included 9 breast cancer risk factors, 5 comorbidities, functional dependency, and mammography use. The model's c-statistic was 0.61 (95 % CI [0.60-0.63]) in NHS and 0.57 (0.55-0.58) in WHI-ES. On average, our model under predicted breast cancer in WHI-ES (E/O 0.92 [0.88-0.97]).

conclusionsWe developed a novel prediction model that factors in postmenopausal women's individualized competing risks of non-breast cancer death when estimating breast cancer risk.

Indexed as

PostmenopauseAdultBreast NeoplasmsCause of DeathFemaleFollow-Up StudiesHumansMiddle AgedModels, StatisticalMortalityPopulation SurveillancePrognosisReproducibility of ResultsRisk AssessmentRisk FactorsBreast cancer predictionCompeting risksOlder

Identifiers

PMID27770283
PMCPMC5093031
OpenAlexW2537898884

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.