The Fred Hutch Biostatistics Program hosts seminars featuring presentations by Fred Hutch and outside scientists to share their latest developments and recent research. Each seminar includes an hour-long presentation and discussion during which speakers showcase their work and findings.
This seminar will be via Zoom.
Predicting risks of chronic diseases has become increasingly important in clinical practice. When a prediction model is developed in a cohort, there is a great interest to apply the model to other cohorts. Due to potential discrepancy in baseline disease incidences 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 new cohort. We assume the relative risks of predictors are the same between the two cohorts, and propose a novel 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 consistency and asymptotic normality of the proposed estimators. Extensive simulation demonstrate 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. The method is illustrated with a recalibration of colorectal cancer prediction model. We also generalize the method accommodating the population-based case-control study and two-phase studies such as the nested case-control study and case-cohort study.