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Bayesian updating: increasing sample size during the course of a study.

Mirjam Moerbeek1

  • 1Department of Methodology and Statistics, Utrecht University, PO Box 80140, 3508 TC, Utrecht, the Netherlands. m.moerbeek@uu.nl.

BMC Medical Research Methodology
|July 6, 2021
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Bayesian updating offers an alternative to traditional sample size calculations by allowing studies to increase sample size as needed. This method avoids the need for a priori effect size estimates, providing flexibility in research design.

Keywords:
Bayes factorError rateInformative hypothesis testing

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Area of Science:

  • Biostatistics
  • Biomedical Research
  • Statistical Methods

Background:

  • A priori sample size calculation demands an accurate effect size estimate, crucial for study power.
  • Inaccurate estimates can lead to underpowered studies or excessive sample sizes.
  • Bayesian updating presents an alternative, enabling dynamic sample size adjustments during a study.

Purpose of the Study:

  • Introduce Bayesian updating to biomedical researchers.
  • Provide insights into expected sample sizes for two-group comparisons using Bayesian updating.
  • Evaluate the error rates associated with Bayesian updating in hypothesis testing.

Main Methods:

  • Bayesian updating utilizes the Bayes factor to quantify evidence for competing hypotheses.
  • The Bayes factor can be updated sequentially as new data become available, without interim analysis corrections.
  • A simulation study was performed to assess expected sample sizes and error rates.

Main Results:

  • Expected sample size is inversely related to effect size and the required degree of evidential support.
  • Higher error rates (incorrect hypothesis support) can occur with low support requirements or small initial sample sizes.
  • Bounded sample sizes may prevent achieving sufficient support for any hypothesis.

Conclusions:

  • Bayesian updating is a valuable alternative to a priori sample size calculation, particularly when subject recruitment is feasible and data accrual is rapid.
  • Simulation results offer guidance on anticipated sample sizes and error rates for Bayesian updating.
  • This approach enhances research efficiency and statistical rigor in biomedical studies.