Imputation and missing indicators for handling missing data in the development and deployment of clinical prediction models: A simulation study.

ABSTRACT: Background: In clinical prediction modelling, missing data can occur at any stage of the model pipeline; development, validation or deployment. Multiple imputation is often recommended yet challenging to apply at deployment; for example, the outcome cannot be in the imputation model, as recommended under multiple imputation. Regression imputation uses a fitted model to impute the predicted value of missing predictors from observed data, and could offer a pragmatic alternative at deployment. Moreover, the use of missing indicators has been proposed to handle informative missingness, but it is currently unknown how well this method performs in the context of clinical prediction models. Methods: We simulated data under various missing data mechanisms to compare the predictive pe

SUBMITTER: Sisk R 

PROVIDER: S-EPMC10515473 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature