Evidence map›Paper›PMID 30732565›Full record

ArticleBMC cancer2019

Risk prediction for breast Cancer in Han Chinese women based on a cause-specific Hazard model.

Lu Wang, Liyuan Liu, Zhen Lou, Lijie Ding, Hui Guan, Fei Wang, Lixiang Yu, Yujuan Xiang, Fei Zhou, Fuzhong Xue and 1 more

Abstract read
In one paragraph

Article in BMC cancer, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 4 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, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Systematic Review and Meta-Analysis of Lifestyle and Reproductive Factors Associated with Risk of Breast Cancer in Asian Women.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2024
    Pooled it
  4. Guideline
  5. Trial
  6. Article
  7. Article
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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.

Lu WangDepartment of Biostatistics, School of Public Health, Shandong University, 44 Wen Hua Xi Road, Jinan, 250012, China.
Liyuan LiuDepartment of Breast Surgery, the Second Hospital of Shandong University, Jinan, 250033, China.
Zhen LouShandong University of Finance and Economics, Jinan, 250014, China.
Lijie DingDivision of Health Management, School of Sport Social Science, Shandong Sport University, Jinan, 250102, China.
Hui GuanDepartment of Radiotherapy, Fourth People's Hospital of Jinan, No 50 Shifan Road, Jinan, 250031, Shangdong, China.
Fei WangDepartment of Breast Surgery, the Second Hospital of Shandong University, Jinan, 250033, China.
Lixiang YuDepartment of Breast Surgery, the Second Hospital of Shandong University, Jinan, 250033, China.
Yujuan XiangDepartment of Breast Surgery, the Second Hospital of Shandong University, Jinan, 250033, China.
Fei ZhouDepartment of Breast Surgery, the Second Hospital of Shandong University, Jinan, 250033, China.
Fuzhong XueDepartment of Biostatistics, School of Public Health, Shandong University, 44 Wen Hua Xi Road, Jinan, 250012, China. xuefzh@sdu.edu.cn.
Zhigang YuDepartment of Breast Surgery, the Second Hospital of Shandong University, Jinan, 250033, China. yzg@medmail.com.cn.ORCID http://orcid.org/0000-0001-6463-4166

Funding

National Natural Science Foundation of China 81602912Science and technology plan projects of Jiangsu province BL2014055
6 · The paper itself

Abstract

backgroundConsidering the lack of efficient breast cancer prediction models suitable for general population screening in China. We aimed to develop a risk prediction model to identify high-risk populations, to help with primary prevention of breast cancer among Han Chinese women.

methodsA cause-specific competing risk model was used to develop the Han Chinese Breast Cancer Prediction model. Data from the Shandong Case-Control Study (328 cases and 656 controls) and Taixing Prospective Cohort Study (13,176 participants) were used to develop and validate the model. The expected/observed (E/O) ratio and C-statistic were calculated to evaluate calibration and discriminative accuracy of the model, respectively.

resultsCompared with the reference level, the relative risks (RRs) for highest level of number of abortions, age at first live birth, history of benign breast disease, body mass index (BMI), family history of breast cancer, and life satisfaction scores were 6.3, 3.6, 4.3, 1.9, 3.3, 2.4, respectively. The model showed good calibration and discriminatory accuracy with an E/O ratio of 1.03 and C-statistic of 0.64.

conclusionsWe developed a risk prediction model including fertility status and relevant disease history, as well as other modifiable risk factors. The model demonstrated good calibration and discrimination ability.

Indexed as

Asian PeopleAdultAgedBreast NeoplasmsCase-Control StudiesChinaFemaleHumansMiddle AgedOdds RatioPopulation SurveillanceProportional Hazards ModelsRisk AssessmentRisk FactorsROC CurveBreast neoplasmsPopulation at riskRisk assessment

Identifiers

PMID30732565
PMCPMC6367757

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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.