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Calculating the sample size required for developing a clinical prediction model

BMJ · 2020 · Vol. 368 · pp. m441–m441
Richard D RileyJoie EnsorKym I E SnellFrank E. HarrellGlen P. MartinJohannes B. ReitsmaKarel G.M. MoonsGary S. CollinsMaarten van Smeden

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.

Meta-analysis and systematic reviewsStatistical Methods in EpidemiologySepsis Diagnosis and TreatmentSample size determinationComputer scienceSample (material)Outcome (game theory)Predictive modellingHealth careData scienceData miningStatisticsMachine learning

MeSH terms

Clinical Decision-MakingForecastingHumansModels, TheoreticalSample Size

Funding

  • Georgia Clinical and Translational Science Alliance
  • National Institute for Health and Care Research
  • Department of Health and Social Care
  • Nederlandse Organisatie voor Wetenschappelijk Onderzoek
  • National Institutes of Health
  • NIHR School for Primary Care Research
  • National Center for Advancing Translational Sciences
Citations
2,352
FWCI
170.79
field-weighted impact
References
80
Percentile
100%
vs. same field & year
Citations per year
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