Krumholz et al. showed that a simple 7-variable risk model performed as well as more complex models in comparing hospital outcomes for AMI.
From a list of potential predictor variables, the final risk model contained seven variables that were retained by the statistical elimination process.
It should be noted that although precise matching of all demographic variables reduces the potential for confounding, it also potentially obscures important effects of variables in a risk model.
The results shown here are applicable to other Mediterranean areas, where modifications are made to the entrance variables in the risk model.
However, it was apparent from initial analyses that the numbers of patients prescribed these new drugs are as yet insufficient as are the numbers of events over time to quantify the effects as separate variables with the risk model.
27 We compared the predictive gain provided by assessment of chronic kidney disease against that provided by information on diabetes and smoking status, removing each of these variables from a risk model containing several conventional risk factors plus chronic kidney disease.
However, while such considerations are germane to the incorporation on KI67 IHC into a multi-variable risk prediction model, issues around analytic validity are not of primary importance in this study.
We calculated the yearly averages of NO2 concentration at all addresses from 1 January 1971 until date of death, censoring or end of follow-up and entered time-weighted average NO2 concentration from 1971 as a time-dependent variable into the statistical risk model, thus recalculating exposure for survivors at the time of each death.
Based on these variables, the SCORE (Systematic COronary Risk Evaluation) risk model predicts fatal CVD events [ 1].
The problem of multicollinearity: Multicollinearity is defined as the intercorrelation between independent variables included in a risk model.
As it occurs at the cut-point that optimises the variable's differentiating ability when equal weight is given to sensitivity and specificity, we used both the Youden index and ROC curves area to determine the cut-off of the continuous variables included in the risk model.
