The development of risk prediction models typically involves several key steps:
Data Collection: Gathering relevant data from large, representative populations. This data should include potential risk factors and outcome variables. Variable Selection: Identifying which variables (risk factors) are most predictive of the outcome of interest. Model Building: Using statistical techniques, such as logistic regression or Cox proportional hazards models, to develop the predictive model. Validation: Testing the model on an independent dataset to assess its predictive accuracy and generalizability.
