Calibrated predictions for multivariate competing risks models

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageAmerican English
Pages (from-to)234-251
Number of pages18
JournalLifetime Data Analysis
Issue number2
StatePublished - Apr 2014

Bibliographical note

Funding Information:
Acknowledgments Malka Gorfine’s work was supported by Israel Science Foundation (ISF) Grant 2012898. Li Hsu’s work was supported by NIH Grants P01 CA53996 and R01AG14358. Giovanni Parmi-giani’s work was supported by NIH/NCI 5P30 CA006516-46 and Komen KG081303.


  • Calibration
  • Competing risks
  • Frailty model
  • Multivariate survival model
  • ROC analysis
  • Risk prediction


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