Key result
A semi-parametric additive Cox model using basis splines and LASSO technique provided more flexibility than standard linear Cox models for predicting survival time in coronary heart disease.
A semi-parametric additive Cox model using basis splines and LASSO offers more flexibility than standard Cox models for predicting coronary heart disease survival time.
May improve survival prediction flexibility in coronary heart disease; leaves open external validation before clinical adoption.
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.
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Yuan et al. (2017) studied Coronary heart disease. Semi-parametric additive Cox model using basis splines and LASSO vs. Standard Cox regression model was evaluated on Survival time. A semi-parametric additive Cox model using basis splines and LASSO technique provided more flexibility than standard linear Cox models for predicting survival time in coronary heart disease.
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