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An additive Cox model for coronary heart disease study

Author

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  • Ao Yuan
  • Yuan Guo
  • Nawar M. Shara
  • Barbara V. Howard
  • Ming T. Tan

Abstract

Existing models for coronary heart disease study use a set of common risk factors to predict the survival time of the disease, via the standard Cox regression model. For complex relationships between the survival time and risk factors, the linear regression specification in the existing Cox model is not flexible enough to accounts for such relationships. Also, the risk factors are actually risky only when they fall in some risk ranges. For more flexibility in modelling and characterize the risk factors more accurately, we study a semi-parametric additive Cox model, using basis splines and LASSO technique. The proposed model is evaluated by simulation studies and is used for the analysis of a real data in the Strong Heart Study.

Suggested Citation

  • Ao Yuan & Yuan Guo & Nawar M. Shara & Barbara V. Howard & Ming T. Tan, 2018. "An additive Cox model for coronary heart disease study," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(7), pages 1325-1346, May.
    • Handle: RePEc:taf:japsta:v:45:y:2018:i:7:p:1325-1346
    DOI: 10.1080/02664763.2017.1369500
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    Cited by:

    1. Jialiang Li & Tonghui Yu & Jing Lv & Mei‐Ling Ting Lee, 2021. "Semiparametric model averaging prediction for lifetime data via hazards regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1187-1209, November.

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