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Prevalence and sample sizes in pre-clinical studies.

Jenna K Felli1, Derek J Leishman1, Meredith A Steeves1

  • 1Affilitation: Eli Lilly and Company, Indianapolis, USA; Eli Lilly and Company, Indianapolis, USA.

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Summary
This summary is machine-generated.

This study shows how event prevalence and sample size affect the chance of observing an event. It offers a new way for study planners to design research based on the risk of not detecting an event.

Keywords:
PrevalenceProbabilitySample size

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Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Understanding the relationship between event prevalence and observation is crucial for study design.
  • Traditional study planning often relies on power calculations, which may not fully capture the nuances of event detection.

Purpose of the Study:

  • To explore the interplay between event prevalence and the probability of observing at least one event in a study of fixed size.
  • To determine the optimal prevalence value that maximizes the impact of adding a single subject to a study.
  • To provide a framework for study design based on the risk of non-detection.

Main Methods:

  • Utilizing the Binomial distribution to model the likelihood of event observation.
  • Analyzing the marginal impact of increasing study size on event observation likelihood.
  • Characterizing the interaction between prevalence and sample size.

Main Results:

  • The probability of observing an event increases with both prevalence and study size.
  • Identified the specific prevalence at which adding one more subject yields the greatest increase in observation likelihood.
  • Demonstrated how prevalence and sample size jointly influence event detection.

Conclusions:

  • The study provides a novel approach to study design, focusing on the risk of non-detection.
  • This method allows for more informed decisions in planning studies, especially for rare events.
  • Offers a valuable tool for researchers to optimize sample size and prevalence considerations.