Calculating the sample size required for developing a clinical prediction model

2020/03/18 by Richard D Riley, Joie Ensor, Kym I E Snell +6 · 38 citations
Decision Sciences · Mathematics · Medicine · #Meta-analysis and systematic reviews #Sepsis Diagnosis and Treatment #Statistical Methods in Epidemiology

paper · pdf · doi:10.1136/bmj.m441

openalex publication_date 2020/03/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

Clinical prediction models aim to predict outcomes in individuals, to inform diagnosis or prognosis in healthcare. Hundreds of prediction models are published in the medical literature each year, yet many are developed using a dataset that is too small for the total number of participants or outcome events. This leads to inaccurate predictions and consequently incorrect healthcare decisions for some individuals. In this article, the authors provide guidance on how to calculate the sample size required to develop a clinical prediction model.

Citations

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• Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration
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• Derivation of a Simple Clinical Model to Categorize Patients Probability of Pulmonary Embolism: Increasing the Models Utility with the SimpliRED D-dimer
• Variable selection – A review and recommendations for the practicing statistician
• Sample size for binary logistic prediction models: Beyond events per variable criteria
• Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes
• PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies

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Related

• A decomposition of Fisher's information to inform sample size for developing or updating fair and precise clinical prediction models -- Part 3: continuous outcomes
• A decomposition of Fisher's information to inform sample size for developing fair and precise clinical prediction models -- part 1: binary outcomes
• Sequential sample size calculations and learning curves safeguard the robust development of a clinical prediction model for individuals
• Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes
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