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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pediatric Research

Background:

  • Recruitment challenges are common in clinical trials, potentially impacting study outcomes.
  • Bayesian analysis offers a method to integrate prior and current evidence, accepted by regulatory bodies.
  • Prior choices in Bayesian inference can heavily influence results, especially in small trials.

Purpose of the Study:

  • To investigate the impact of prior parameter choices on Bayesian inference in small clinical trials.
  • To evaluate the effect of discounting factors on the Renal Scarring Urinary Infection (RESCUE) trial data.
  • To determine optimal prior settings for reliable inference in underrecruited trials.

Main Methods:

  • Simulated 50 scenarios with varying sample sizes and absolute risk reduction (ARR) for the RESCUE trial.
  • Applied Bayesian analysis with 0%, 50%, and 100% discounting factors on the beta power prior.
  • Assessed the influence of prior choices on data sensitivity and outcome confirmation probability.

Main Results:

  • Informative priors (0% discounting) yielded data-insensitive results in small samples.
  • A 50% discounting factor increased the probability of confirming trial outcomes to over 80% for ARR > 0.17.
  • Prior parameter selection significantly affects the reliability of Bayesian inference in small trials.

Conclusions:

  • Adjusting prior parameters, such as through discounting factors, is crucial for maintaining data relevance in small trial Bayesian analysis.
  • Defining discounting factors based on prior parameters can enhance inference.
  • Sensitivity analysis for prior choices is highly recommended for robust Bayesian trial conclusions.