Internal-external cross-validation helped to evaluate the generalizability of prediction models in large clustered datasets.

Abstract

OBJECTIVE: To illustrate how to evaluate the need of complex strategies for developing generalizable prediction models in large clustered datasets. STUDY DESIGN AND SETTING: We developed eight Cox regression models to estimate the risk of heart failure using a large population-level dataset. These models differed in the number of predictors, the functional form of the predictor effects (non-linear effects and interaction) and the estimation method (maximum likelihood and penalization). Internal-external cross-validation was used to evaluate the models' generalizability across the included general practices. RESULTS: Among 871,687 individuals from 225 general practices, 43,987 (5.5%) developed heart failure during a median follow-up time of 5.8 years. For discrimination, the simplest prediction model yielded a good concordance statistic, which was not much improved by adopting complex strategies. Between-practice heterogeneity in discrimination was similar in all models. For calibration, the simplest model performed satisfactorily. Although accounting for non-linear effects and interaction slightly improved the calibration slope, it also led to more heterogeneity in the observed/expected ratio. Similar results were found in a second case study involving patients with stroke. CONCLUSION: In large clustered datasets, prediction model studies may adopt internal-external cross-validation to evaluate the generalizability of competing models, and to identify promising modelling strategies.

Citation

(2021). Internal-external cross-validation helped to evaluate the generalizability of prediction models in large clustered datasets. Journal of Clinical Epidemiology, 83 - 91. https://doi.org/10.1016/j.jclinepi.2021.03.025

• Acceptance Date: Mar 29, 2021
• Publication Date: Apr 6, 2021
• Journal: Journal of Clinical Epidemiology
• Print ISSN: 0895-4356
• Publisher: Elsevier
• Pages: 83 - 91
• DOI: https://doi.org/10.1016/j.jclinepi.2021.03.025
• Keywords: Prediction model, Calibration, Discrimination, Validation, Heterogeneity, Model comparison
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0895435621001074