Handling missing predictor values when validating and applying a prediction model to new patients.

ABSTRACT: Missing data present challenges for development and real-world application of clinical prediction models. While these challenges have received considerable attention in the development setting, there is only sparse research on the handling of missing data in applied settings. The main unique feature of handling missing data in these settings is that missing data methods have to be performed for a single new individual, precluding direct application of mainstay methods used during model development. Correspondingly, we propose that it is desirable to perform model validation using missing data methods that transfer to practice in single new patients. This article compares existing and new methods to account for missing data for a new individual in the context of prediction. These methods ar

SUBMITTER: Hoogland J 

PROVIDER: S-EPMC7586995 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature