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Bayesian methods for pilot studies.

Andrew R Willan1,2, Lehana Thabane3,4

  • 1Ontario Child Health Support Unit, SickKids Research Institute, Toronto, ON, Canada.

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|April 17, 2020

View abstract on PubMed

Summary
This summary is machine-generated.

Bayesian methods offer a more meaningful way to analyze pilot study data for randomized controlled trials. This approach accurately quantifies uncertainty in feasibility parameters, improving trial design and power.

Keywords:
Bayesian methodsPilot studiescompliance feasibilityfollow-up feasibilityrecruitment feasibility

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Health Research Methodology

Background:

  • Pilot studies are increasingly used to inform randomized controlled trial (RCT) design.
  • Feasibility parameters like recruitment, compliance, and follow-up probabilities are estimated from pilot studies.
  • Frequentist confidence intervals inadequately measure uncertainty for RCT design.

Purpose of the Study:

  • Introduce Bayesian methods for analyzing pilot study data.
  • Determine the feasibility of associated randomized controlled trials using pilot data.
  • Provide a meaningful quantification of uncertainty in feasibility parameters.

Main Methods:

  • Illustrate Bayesian approach advantages using a literature example.
  • Employ vague beta distribution priors for feasibility parameters.
  • Utilize simulation methods to determine recruitment strategy power for RCTs.

    • Main Results:

    • Vague priors for feasibility parameters demonstrate robustness.
    • Beta-binomial predictive distributions are derived for RCT outcomes.
    • Ignoring pilot study uncertainty can lead to insufficient RCT power.

    Conclusions:

    • Bayesian methods provide direct inference and intuitive quantification of uncertainty in pilot study feasibility.
    • Bayesian analysis enhances the design of randomized controlled trials.
    • Bayesian approaches can identify optimal recruitment strategies for desired RCT power.