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Related Experiment Videos

Adaptive Gaussian process search for simulation-based sample size estimation in clinical prediction models:

Oyebayo Ridwan Olaniran1,2, Diana Shamsutdinova3,4, Sarah Markham3

  • 1Department of Biostatistics & Health Informatics, King's College London, London, UK. ridwan.olaniran@kcl.ac.uk.

BMC Medical Research Methodology
|July 12, 2026
PubMed
Summary

The pmsims R package offers a flexible and efficient simulation-based framework for determining sample sizes in clinical prediction models. It reliably achieves performance targets across various scenarios, outperforming existing methods.

Keywords:
R packageAdaptive simulationAssuranceCalibrationDiscriminationGaussian processPrediction modellingSample size planning

Related Experiment Videos

Area of Science:

  • Statistics
  • Machine Learning
  • Biostatistics

Background:

  • Determining adequate sample size is crucial for reliable clinical prediction models.
  • Current methods for sample size calculation are limited, often imposing restrictive assumptions.
  • Existing tools focus on mean-based criteria, lacking flexibility for diverse prediction models.

Purpose of the Study:

  • To introduce and validate the pmsims R package for sample size determination.
  • To provide a flexible and efficient simulation-based framework using Gaussian process (GP) surrogate modelling.
  • To validate the framework across binary, continuous, and survival prediction modelling contexts.

Main Methods:

  • A comprehensive simulation study compared three search engines within pmsims: GP-based adaptive procedure (gp), bisection method (bisection), and hybrid GP-bisection (gp-bs).
  • Scenarios varied outcome prevalence, predictor dimensionality, performance metrics (discrimination, calibration), aggregation criteria, and simulation budget.
  • The best pmsims engine was benchmarked against analytical (pmsampsize) and simulation-based (samplesizedev) methods using independent validation datasets.

Main Results:

  • The GP-based search engine in pmsims consistently provided the most stable sample size estimates across all outcome types.
  • pmsims demonstrated computational efficiency, achieving performance deviations within 0.01 of target across model types.
  • pmsims substantially outperformed pmsampsize in high-discrimination settings and was comparable to samplesizedev.

Conclusions:

  • The pmsims package, utilizing the GP-based search engine, offers an efficient and flexible framework for sample size planning in clinical prediction modelling.
  • It reliably achieves performance targets across standard model scenarios with fewer evaluations than non-adaptive methods.
  • pmsims presents a compelling alternative to traditional analytical formulae and exhaustive simulation-based approaches for sample size determination.