A simple question on the development of risk prediction models from Cox regressions.
Suppose, as an example, that I want to create a risk score for 1-year mortality in patients with cardiovascular disease. Performing a Cox regression, I found 3 dichotomous variables (yes/no; lets call them variable1, variable2 and variable3 for simplicity), independently associated with the outcome. Each of these variables has a beta coefficient within the regression model.
In order to develop a scoring system to predict the risk of mortality at 1 year, I need to assign a score to the presence of each variable. Obviously, points within the score can (and should) be "weighted" according to the importance of the predictor on the outcome (e.g., variable 1 may give 1 point, while variable3 may give more points, if present).
Which approach should be used to establish how many point should be assigned to each variable in the Cox-Regression? One approach (perhaps naive) would be to transform the beta-coefficients into scores, but I would like to know if there is a more "rigorous" method, and especially if there are reference papers to use as guidance.