Application of the Cox model as a predictor of relative risk of coronary heart disease in the albany study

Patients in long term studies of coronary heart disease may have different levels of risk during the course of study. Smoking habits, blood pressure, and obesity may change drastically during this period. The multiple logistic model, the most commonly used model for the analysis of coronary heart disease studies, does not consider survival time in assessment of the dependent covariates and does not account for the censoring which usually occurs in such studies. We propose a Cox model with time-dependent covariates to model the risk of coronary heart disease in the Albany study. The Cox model we fitted evaluates the patients' risk on the basis of the data at the last visit. With this methodology, we can evaluate whether it is advantageous for individuals to modify their risk of disease by their effecting changes in their covariates, that is to stop smoking, lose weight, change diet and so on. The important covariates that explain the risk of coronary heart disease were the same in our model as in the models used in the earlier reports. The estimated relative risks were slightly higher in most cases and lend more support to the need to encourage patients to achieve a better covariate state.