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Abstract:
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Risk prediction for chronic diseases has become increasingly important in clinical practice. When a prediction model is developed in a cohort, there is often a great interest to apply the model to other cohorts. However, due to potential discrepancy between different cohorts and shifts in patient composition, the risk predicted by the model built in the source cohort often under- or over-estimates the risk in a target cohort. In this talk, I will present a weighted estimating equation approach to re-calibrating the projected risk for the targeted population through updating the baseline risk. The recalibration leverages the knowledge about survival probabilities for the disease of interest and competing events, and summary information of risk factors from the target population. We establish the asymptotic properties and show through simulation that the proposed estimators are robust, even if the risk factor distributions differ between the source and target populations, and gain efficiency if they are the same, as long as the information from the target is precise. We illustrate the method by recalibration of a colorectal cancer prediction model.
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