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Published on: September 16, 2022
Comparison of Bayesian and frequentist monitoring boundaries motivated by the Multiplatform Randomized Clinical Trial
Jungnam Joo1, Eric S Leifer1, Michael A Proschan2
1Office of Biostatistics Research, Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, MD, USA.
Insights
Bayesian and frequentist clinical trial monitoring methods can both achieve rapid efficacy or futility decisions. Aggressive monitoring, whether Bayesian or frequentist, is crucial in pandemics for quickly identifying effective treatments.
Area of Science:
- Clinical Trials
- Biostatistics
- Epidemiology
Background:
- The COVID-19 pandemic emphasized the need for efficient clinical trial monitoring.
- Traditional frequentist methods and Bayesian approaches are used for interim efficacy and futility analyses.
- Bayesian methods are often believed to offer quicker decisions compared to frequentist approaches.
Purpose of the Study:
- To compare Bayesian and frequentist interim monitoring guidelines for randomized clinical trials.
- To evaluate the belief that Bayesian methods lead to faster efficacy/futility decisions.
- To analyze the similarity between Bayesian and frequentist efficacy boundaries.
Main Methods:
- Interpreted Bayesian methods as combining prior beliefs with actual trial data.
- Examined the Multiplatform Randomized Clinical Trial (mpRCT) Bayesian guidelines.
- Compared Bayesian efficacy boundaries (99% probability threshold) with frequentist Pocock and O'Brien-Fleming guidelines.
- Contrasted Bayesian futility guidelines with frequentist conditional power guidelines.
Main Results:
- Bayesian efficacy boundaries with a 99% probability threshold closely resemble frequentist Pocock boundaries.
- Bayesian monitoring with a neutral prior is more aggressive than O'Brien-Fleming combined with 20% conditional power futility.
- More aggressive boundaries can lead to earlier trial cessation but may reduce statistical power.
Conclusions:
- Aggressive monitoring is advantageous in pandemics for rapid treatment evaluation.
- Both Bayesian and frequentist methods can implement aggressive monitoring strategies.
- The choice between Bayesian and frequentist approaches depends on the specific goals and context of the trial.
Background:
The coronavirus disease 2019 pandemic highlighted the need to conduct efficient randomized clinical trials with interim monitoring guidelines for efficacy and futility. Several randomized coronavirus disease 2019 trials, including the Multiplatform Randomized Clinical Trial (mpRCT), used Bayesian guidelines with the belief that they would lead to quicker efficacy or futility decisions than traditional "frequentist" guidelines, such as spending functions and conditional power. We explore this belief using an intuitive interpretation of Bayesian methods as translating prior opinion about the treatment effect into imaginary prior data. These imaginary observations are then combined with actual observations from the trial to make conclusions. Using this approach, we show that the Bayesian efficacy boundary used in mpRCT is actually quite similar to the frequentist Pocock boundary.
Methods:
The mpRCT's efficacy monitoring guideline considered stopping if, given the observed data, there was greater than 99% probability that the treatment was effective (odds ratio greater than 1). The mpRCT's futility monitoring guideline considered stopping if, given the observed data, there was greater than 95% probability that the treatment was less than 20% effective (odds ratio less than 1.2). The mpRCT used a normal prior distribution that can be thought of as supplementing the actual patients' data with imaginary patients' data. We explore the effects of varying probability thresholds and the prior-to-actual patient ratio in the mpRCT and compare the resulting Bayesian efficacy monitoring guidelines to the well-known frequentist Pocock and O'Brien-Fleming efficacy guidelines. We also contrast Bayesian futility guidelines with a more traditional 20% conditional power futility guideline.
Results:
A Bayesian efficacy and futility monitoring boundary using a neutral, weakly informative prior distribution and a fixed probability threshold at all interim analyses is more aggressive than the commonly used O'Brien-Fleming efficacy boundary coupled with a 20% conditional power threshold for futility. The trade-off is that more aggressive boundaries tend to stop trials earlier, but incur a loss of power. Interestingly, the Bayesian efficacy boundary with 99% probability threshold is very similar to the classic Pocock efficacy boundary.
Conclusions:
In a pandemic where quickly weeding out ineffective treatments and identifying effective treatments is paramount, aggressive monitoring may be preferred to conservative approaches, such as the O'Brien-Fleming boundary. This can be accomplished with either Bayesian or frequentist methods.
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