B1348
Title: A constrained maximum likelihood approach to developing well-calibrated risk prediction models
Authors: Jinbo Chen - University of Pennsylvania (United States) [presenting]
Abstract: The added value of candidate predictors for risk modelling is routinely evaluated by comparing the performance of models with or without including candidate predictors. Such comparison is most meaningful when the estimated risk is unbiased in the target population. Oftentimes, data for standard predictors in the base model is richly available from the target population, but data for candidate predictors are available only from nonrepresentative convenience samples. While the base model can be naively updated using the study data without recognizing the discrepancy between the underlying distribution of the study data and that in the target population, the resultant risk estimates and the evaluation of the candidate predictors are biased. To this end, a semiparametric method is proposed for model fitting that allows unbiased assessment of model improvement without requiring a representative sample from the target population, thereby overcoming a major bottleneck in practice. The proposed method is applied to data extracted from Penn Medicine Biobank to inform the added value of breast density for breast cancer risk assessment in the Caucasian woman population.
