Accommodating population differences when validating risk prediction models.

ABSTRACT: Validation of risk prediction models in independent data provides a more rigorous assessment of model performance than internal assessment, for example, done by cross-validation in the data used for model development. However, several differences between the populations that gave rise to the training and the validation data can lead to seemingly poor performance of a risk model. In this paper we formalize the notions of "similarity" or "relatedness" of the training and validation data, and define reproducibility and transportability. We address the impact of different distributions of model predictors and differences in verifying the disease status or outcome on measures of calibration, accuracy and discrimination of a model. When individual level information from both the training and val

SUBMITTER: Pfeiffer RM 

PROVIDER: S-EPMC10510530 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature