An additive Cox model for coronary heart disease study

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.

KEYWORDS:

• Additive model
• B-spline
• coronary heart disease
• Cox model
• LASSO
• risk factors
• survival times

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Tan's research is partially supported by the National Institutes of Health grant R01CA164717.