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Multicentre randomised trials in anaesthesia: an analysis using Bayesian metrics.

M Seretny1,1, J Barlow2, D Sidebotham1,1

  • 1Department of Anaesthesia, Auckland City Hospital, Auckland, New Zealand.

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|September 21, 2022
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Summary
This summary is machine-generated.

Bayesian methods offer deeper insights into randomized trial results than traditional p values and confidence intervals. This study suggests inadequate power and mortality outcomes contribute to non-significant findings in anesthesia research.

Keywords:
Bayes factorBayes' theoremanaesthesiaresearch designsample sizesignificance testing

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Area of Science:

  • Medical research methodology
  • Statistical analysis in clinical trials
  • Anesthesiology research

Background:

  • Randomized trials are crucial for evaluating medical interventions.
  • P values and confidence intervals are standard but may not fully capture trial evidence.
  • Low statistical power is a common issue in medical research, potentially leading to false negatives.

Purpose of the Study:

  • To evaluate the reliability of randomized trial results using Bayesian methods.
  • To compare the insights provided by Bayesian metrics versus traditional p values and confidence intervals.
  • To assess the impact of study power and outcome selection on trial declarations of efficacy.

Main Methods:

  • Structured review of multicenter anesthesia trials published in top medical journals (2011-2021).
  • Documentation of declared effects, expected, and observed effect sizes.
  • Calculation of Bayes factors and post-test probabilities of zero effect.
  • Contacted authors to estimate trial costs.

Main Results:

  • Median hypothesized effect size was 7% vs. observed 2%.
  • Only 21% (12/56) of primary outcomes declared a non-zero effect.
  • Bayes factors indicated substantial evidence for a zero effect in many trials.
  • Post-test probabilities of zero effect ranged from 0.0001% to 99% across trials.
  • Median trial cost exceeded $1.4 million USD.

Conclusions:

  • Inadequate statistical power and the use of mortality as an outcome may explain the low rate of declared non-zero effects.
  • Bayes factors and post-test probabilities offer valuable insights into trial data, especially when p values are near significance thresholds.
  • Bayesian approaches can complement traditional statistics for a more nuanced interpretation of clinical trial results.