ArticleLifetime data analysis2014
Calibrated predictions for multivariate competing risks models.
Article in Lifetime data analysis, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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
9 citing papers in PubMed, 14 citations in OpenAlex.
- Semiparametric regression analysis of bivariate censored events in a family study of Alzheimer's disease.Biostatistics (Oxford, England) · 2022Article
- A competing risks model with binary time varying covariates for estimation of breast cancer risks inStatistical methods in medical research · 2021Article
- Variation in cancer risk among families with genetic susceptibility.Genetic epidemiology · 2021Article
- Practical implementation of frailty models in Mendelian risk prediction.Genetic epidemiology · 2020Article
- A risk score for predicting atrial fibrillation in individuals with preclinical diastolic dysfunction: a retrospective study in a single large urban center in the United States.BMC cardiovascular disorders · 2019Article
- Bayesian Semiparametric Estimation of Cancer-specific Age-at-onset Penetrance with Application to Li-Fraumeni Syndrome.Journal of the American Statistical Association · 2019Article
- Evaluating center performance in the competing risks setting: Application to outcomes of wait-listed end-stage renal disease patients.Biometrics · 2018Article
- Hierarchical models for semi-competing risks data with application to quality of end-of-life care for pancreatic cancer.Journal of the American Statistical Association · 2016Article
- Frailty Models for Familial Risk with Application to Breast Cancer.Journal of the American Statistical Association · 2013Article
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
Abstract
Prediction models for time-to-event data play a prominent role in assessing the individual risk of a disease, such as cancer. Accurate disease prediction models provide an efficient tool for identifying individuals at high risk, and provide the groundwork for estimating the population burden and cost of disease and for developing patient care guidelines. We focus on risk prediction of a disease in which family history is an important risk factor that reflects inherited genetic susceptibility, shared environment, and common behavior patterns. In this work family history is accommodated using frailty models, with the main novel feature being allowing for competing risks, such as other diseases or mortality. We show through a simulation study that naively treating competing risks as independent right censoring events results in non-calibrated predictions, with the expected number of events overestimated. Discrimination performance is not affected by ignoring competing risks. Our proposed prediction methodologies correctly account for competing events, are very well calibrated, and easy to implement.
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What OpenQuestion holds
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.