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Estimating Risk With Time-to-Event Data: An Application to the Women's Health Initiative

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  • Dandan Liu
  • Yingye Zheng
  • Ross L. Prentice
  • Li Hsu

Abstract

Accurate and individualized risk prediction is critical for population control of chronic diseases such as cancer and cardiovascular disease. Large cohort studies provide valuable resources for building risk prediction models, as the risk factors are collected at the baseline and subjects are followed over time until disease occurrence or termination of the study. However, for rare diseases the baseline risk may not be estimated reliably based on cohort data only, due to sparse events. In this article, we propose to make use of external information to improve efficiency for estimating time-dependent absolute risk. We derive the relationship between external disease incidence rates and the baseline risk, and incorporate the external disease incidence information into estimation of absolute risks, while allowing for potential difference of disease incidence rates between cohort and external sources. The asymptotic properties, namely, uniform consistency and weak convergence, of the proposed estimators are established. Simulation results show that the proposed estimator for absolute risk is more efficient than that based on the Breslow estimator, which does not use external disease incidence rates. A large cohort study, the Women's Health Initiative Observational Study, is used to illustrate the proposed method. Supplementary materials for this article are available online.

Suggested Citation

  • Dandan Liu & Yingye Zheng & Ross L. Prentice & Li Hsu, 2014. "Estimating Risk With Time-to-Event Data: An Application to the Women's Health Initiative," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 514-524, June.
  • Handle: RePEc:taf:jnlasa:v:109:y:2014:i:506:p:514-524
    DOI: 10.1080/01621459.2014.881739
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    Cited by:

    1. Yu‐Jen Cheng & Yen‐Chun Liu & Chang‐Yu Tsai & Chiung‐Yu Huang, 2023. "Semiparametric estimation of the transformation model by leveraging external aggregate data in the presence of population heterogeneity," Biometrics, The International Biometric Society, vol. 79(3), pages 1996-2009, September.
    2. Jiayin Zheng & Yingye Zheng & Li Hsu, 2022. "Re‐calibrating pure risk integrating individual data from two‐phase studies with external summary statistics," Biometrics, The International Biometric Society, vol. 78(4), pages 1515-1529, December.

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