[2602.23507] Sample Size Calculations for Developing Clinical Prediction Models: Overview and pmsims R package

Computer Science > Machine Learning arXiv:2602.23507 (cs) [Submitted on 26 Feb 2026] Title:Sample Size Calculations for Developing Clinical Prediction Models: Overview and pmsims R package Authors:Diana Shamsutdinova, Felix Zimmer, Oyebayo Ridwan Olaniran, Sarah Markham, Daniel Stahl, Gordon Forbes, Ewan Carr View a PDF of the paper titled Sample Size Calculations for Developing Clinical Prediction Models: Overview and pmsims R package, by Diana Shamsutdinova and 6 other authors View PDF Abstract:Background: Clinical prediction models are increasingly used to inform healthcare decisions, but determining the minimum sample size for their development remains a critical and unresolved challenge. Inadequate sample sizes can lead to overfitting, poor generalisability, and biased predictions. Existing approaches, such as heuristic rules, closed-form formulas, and simulation-based methods, vary in flexibility and accuracy, particularly for complex data structures and machine learning models. Methods: We review current methodologies for sample size estimation in prediction modelling and introduce a conceptual framework that distinguishes between mean-based and assurance-based criteria. Building on this, we propose a novel simulation-based approach that integrates learning curves, Gaussian Process optimisation, and assurance principles to identify sample sizes that achieve target performance with high probability. This approach is implemented in pmsims, an open-source, model-agnost...