ArticleStatistics in medicine2008
Multiple diseases in carrier probability estimation: accounting for surviving all cancers other than breast and ovary in BRCAPRO.
Article in Statistics in medicine, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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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.
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Who cites it
12 citing papers in PubMed, 18 citations in OpenAlex.
- Statistical methods for Mendelian models with multiple genes and cancers.Genetic epidemiology · 2022Article
- A pedigree-based prediction model identifies carriers of deleterious de novo mutations in families with Li-Fraumeni syndrome.Genome research · 2020Article
- A Pragmatic Testing-Eligibility Framework for Population Mutation Screening: The Example ofCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2019Article
- Simpson's paradox in the integrated discrimination improvement.Statistics in medicine · 2017Article
- EstimatingCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2017Article
- Modeling of successive cancer risks in Lynch syndrome families in the presence of competing risks using copulas.Biometrics · 2017Article
- A two-stage approach to genetic risk assessment in primary care.Breast cancer research and treatment · 2016Article
- Reclassification of predictions for uncovering subgroup specific improvement.Statistics in medicine · 2014Article
- Calibrated predictions for multivariate competing risks models.Lifetime data analysis · 2014Article
- Providing access to risk prediction tools via the HL7 XML-formatted risk web service.Breast cancer research and treatment · 2013Article
- Simplifying clinical use of the genetic risk prediction model BRCAPRO.Breast cancer research and treatment · 2013Article
- Frailty-based competing risks model for multivariate survival data.Biometrics · 2011Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Mendelian models can predict who carries an inherited deleterious mutation of known disease genes based on family history. For example, the BRCAPRO model is commonly used to identify families who carry mutations of BRCA1 and BRCA2, based on familial breast and ovarian cancers. These models incorporate the age of diagnosis of diseases in relatives and current age or age of death. We develop a rigorous foundation for handling multiple diseases with censoring. We prove that any disease unrelated to mutations can be excluded from the model, unless it is sufficiently common and dependent on a mutation-related disease time. Furthermore, if a family member has a disease with higher probability density among mutation carriers, but the model does not account for it, then the carrier probability is deflated. However, even if a family only has diseases the model accounts for, if the model excludes a mutation-related disease, then the carrier probability will be inflated. In light of these results, we extend BRCAPRO to account for surviving all non-breast/ovary cancers as a single outcome. The extension also enables BRCAPRO to extract more useful information from male relatives. Using 1500 families from the Cancer Genetics Network, accounting for surviving other cancers improves BRCAPRO's concordance index from 0.758 to 0.762 (p=0.046), improves its positive predictive value from 35 to 39 per cent (p<10(-6)) without impacting its negative predictive value, and improves its overall calibration, although calibration slightly worsens for those with carrier probability<10 per cent.
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Registered trials
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.