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Non-parametric Bayesian Hazard Regression for Chronic Disease Risk Assessment

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  • Olli Saarela
  • Elja Arjas

Abstract

type="main" xml:id="sjos12125-abs-0001"> Assessing the absolute risk for a future disease event in presently healthy individuals has an important role in the primary prevention of cardiovascular diseases (CVD) and other chronic conditions. In this paper, we study the use of non-parametric Bayesian hazard regression techniques and posterior predictive inferences in the risk assessment task. We generalize our previously published Bayesian multivariate monotonic regression procedure to a survival analysis setting, combined with a computationally efficient estimation procedure utilizing case–base sampling. To achieve parsimony in the model fit, we allow for multidimensional relationships within specified subsets of risk factors, determined either on a priori basis or as a part of the estimation procedure. We apply the proposed methods for 10-year CVD risk assessment in a Finnish population. © 2014 Board of the Foundation of the Scandinavian Journal of Statistics

Suggested Citation

  • Olli Saarela & Elja Arjas, 2015. "Non-parametric Bayesian Hazard Regression for Chronic Disease Risk Assessment," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(2), pages 609-626, June.
    • Handle: RePEc:bla:scjsta:v:42:y:2015:i:2:p:609-626
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    File URL: http://hdl.handle.net/10.1111/sjos.12125
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    References listed on IDEAS

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    1. Hanley James A & Miettinen Olli S, 2009. "Fitting Smooth-in-Time Prognostic Risk Functions via Logistic Regression," The International Journal of Biostatistics, De Gruyter, vol. 5(1), pages 1-25, January.
    2. Yingye Zheng & Patrick Heagerty, 2004. "Semiparametric Estimation of Time-Dependent: ROC Curves for Longitudinal Marker Data," UW Biostatistics Working Paper Series 1052, Berkeley Electronic Press.
    3. Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
    4. Patrick J. Heagerty & Thomas Lumley & Margaret S. Pepe, 2000. "Time-Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker," Biometrics, The International Biometric Society, vol. 56(2), pages 337-344, June.
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    1. Olli Saarela, 2016. "A case-base sampling method for estimating recurrent event intensities," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(4), pages 589-605, October.

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