Calibrated predictions for multivariate competing risks models View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-04

AUTHORS

Malka Gorfine, Li Hsu, David M. Zucker, Giovanni Parmigiani

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. More... »

PAGES

234-251

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10985-013-9260-x

DOI

http://dx.doi.org/10.1007/s10985-013-9260-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1007322350

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/23737081


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