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Statistical primer: sample size considerations for developing and validating clinical prediction models.
Glen P Martin1, Richard D Riley2,3, Joie Ensor2,3
1Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.
Summary
Ensuring adequate sample size is crucial for developing reliable clinical prediction models. This primer overviews sample size calculation methods, required data, and available software for accurate model development and validation.
Area of Science:
- Biostatistics
- Clinical Epidemiology
- Health Informatics
Background:
- Clinical prediction models (CPMs) integrate individual data to estimate health risks.
- Inadequate sample sizes can lead to unstable models and imprecise performance estimates, potentially harming clinical practice.
- Recent advancements include specific formulae for minimum sample size estimation in CPM development and validation.
Purpose of the Study:
- To provide a statistical primer on sample size calculation criteria for CPMs.
- To detail the information needed for these calculations.
- To illustrate the implementation of sample size methods with practical examples and review available software.
Main Methods:
- Overview of recently developed sample size formulae for CPM development and external validation.
- Description of data requirements for sample size calculations.
- Illustrative worked examples and software review for implementing sample size criteria.
Main Results:
- The primer provides a comprehensive overview of essential sample size calculation methods for CPMs.
- It details the necessary inputs for performing these calculations accurately.
- Worked examples and software reviews facilitate practical application and implementation.
Conclusions:
- Adequate sample size is fundamental for the stability and precise performance estimation of CPMs.
- This primer equips researchers with the knowledge and tools to determine appropriate sample sizes for model development and validation.
- Utilizing the presented methods and software can enhance the clinical utility and safety of prediction models.
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