Risk Projection for Time-to-event Outcome Leveraging Summary Statistics With Source Individual-level Data.

ABSTRACT: 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. In this article, 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 a

SUBMITTER: Zheng J 

PROVIDER: S-EPMC9855229 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature