Clinical Equipoise and Adaptive Clinical Trials

Fillion, Nicolas

doi:10.1007/s11245-018-9540-x

Cited by 3 publications

(6 citation statements)

References 22 publications

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“…Despite the vast methodological investigation, BAR has received much debate and criticism in the biostatistical community. 10,16–23 Most of the discussions focus on the statistical properties of BAR when making comparisons with other randomization approaches, such as those that have fixed allocation probabilities. An undesirable property of this approach is that the procedure would randomize patients to an inferior arm in some circumstances, 4,10 which has raised concerns in its application.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Bayesian adaptive randomization procedures with adaptive clip methods for multi-arm trials

Lee

2021

Stat Methods Med Res

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Bayesian adaptive randomization is a heuristic approach that aims to randomize more patients to the putatively superior arms based on the trend of the accrued data in a trial. Many statistical aspects of this approach have been explored and compared with other approaches; yet only a limited number of works has focused on improving its performance and providing guidance on its application to real trials. An undesirable property of this approach is that the procedure would randomize patients to an inferior arm in some circumstances, which has raised concerns in its application. Here, we propose an adaptive clip method to rectify the problem by incorporating a data-driven function to be used in conjunction with Bayesian adaptive randomization procedure. This function aims to minimize the chance of assigning patients to inferior arms during the early time of the trial. Moreover, we propose a utility approach to facilitate the selection of a randomization procedure. A cost that reflects the penalty of assigning patients to the inferior arm(s) in the trial is incorporated into our utility function along with all patients benefited from the trial, both within and beyond the trial. We illustrate the selection strategy for a wide range of scenarios.

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Section: Introductionmentioning

confidence: 99%

Evaluating Bayesian adaptive randomization procedures with adaptive clip methods for multi-arm trials

Lee

2021

Stat Methods Med Res

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“…There is a broad literature on the methodological and statistical aspects of OAR. More recently, a number of articles have addressed some of its ethical implications [ 2 – 13 ]. This paper seeks to contribute to this discussion in the context of a simple two-arm RCT, 1 with a particular focus on two central ethical issues: welfare and autonomy.…”

Section: Introductionmentioning

confidence: 99%

“…Crucially, if the demands of equipoise (on either definition) are satisfied, patients are not knowingly disadvantaged by being randomized to one arm of the trial rather than another. Nicolas Fillion [ 13 ] points out that an additional requirement on a trial is that it should be capable of disturbing—or at least contributing to disturbing—the state of equipoise that existed at the outset; so equipoise must exist, but it must also be assailable.…”

Section: Introductionmentioning

confidence: 99%

“…Equipoise has been discussed by several authors in specific relation to OAR [ 2 , 9 – 13 ]. However, an analysis of the ethics of OAR depends crucially on how equipoise is conceived.…”

Section: Introductionmentioning

confidence: 99%

“…This, however, would count in favour of OAR only if one were to accept a consequentialist moral calculus that permits a direct trade-off between benefits and harms—a calculus that is out of keeping with the deontological basis of individual ethics, which would place some degree of prohibition on harm even in the face of a greater countervailing benefit. Moreover, setting aside the distribution of participants across the arms of the trial, any increase in the sample size, and the associated costs of a study, raises morally relevant issues of efficiency [ 2 , 13 , 44 ]. 8…”

Section: Introductionmentioning

confidence: 99%

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Outcome-adaptive randomization in clinical trials: issues of participant welfare and autonomy

Sim

2019

Theor Med Bioeth

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Outcome-adaptive randomization (OAR) has been proposed as a corrective to certain ethical difficulties inherent in the traditional randomized clinical trial (RCT) using fixed-ratio randomization. In particular, it has been suggested that OAR redresses the balance between individual and collective ethics in favour of the former. In this paper, I examine issues of welfare and autonomy arising in relation to OAR. A central issue in discussions of welfare in OAR is equipoise, and the moral status of OAR is crucially influenced by the way in which this concept is construed. If OAR is based on a model of equipoise that demands strict indifference between competing interventions throughout the trial, such equipoise is disturbed by accruing data favouring one treatment over another; OAR seeks to redress this by weighting randomization to the seemingly superior treatment. However, this is a partial response, as patients continue to be allocated to the inferior therapy. Moreover, it rests upon considerations of aggregate harms and benefits, and does not therefore uphold individual ethics. Issues of fairness also arise, as early and late enrollees are randomized on a different basis. Fixed-ratio randomization represents a fuller and more consistent response to a loss of equipoise, as so construed. With regard to consent, the complexity of OAR poses challenges to adequate disclosure and comprehension. Additionally, OAR does not offer a remedy to the therapeutic misconception—participants’ tendency to attribute treatment allocation in an RCT to individual clinical judgments, rather than to scientific considerations—and, if anything, accentuates rather than alleviates this misconception. In relation to these issues, OAR fails to offer ethical advantages over fixed-ratio randomization. More broadly, the ethical basis of OAR can be seen to lie more in collective than in individual ethics, and overall it fares worse in this territory than fixed-ratio randomization.

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Learning with insufficient data: a multi-armed bandit perspective on covid-19 interventions

Ortega¹

2022

Mind Soc

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In February 2020, as covid-19 infections spread to more than fifty countries, public health officials needed to recommend how the public could protect themselves, balancing safety and urgency. But there was very little data since this novel virus had only been identified three months prior. How could public health officials decide with insufficient data? The multi-armed bandit problem of computer science offers adaptive decision-making procedures that can achieve both safety and urgency. These adaptive methods balance learning information (exploring) with using information (exploiting), adjusting the balance toward learning when uncertainty is high (March 1991 ; Kaelbling et al. 1996 ). Related methods are already used in adaptive clinical trials for pharmaceuticals (Pallmann et al. 2018 ). But we still need to develop these methods for non-pharmaceutical interventions, as I will illustrate with a case study of public mask-wearing to reduce the spread of covid-19. Public health pronouncements impact future learning.

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Clinical Equipoise and Adaptive Clinical Trials

Cited by 3 publications

References 22 publications

Evaluating Bayesian adaptive randomization procedures with adaptive clip methods for multi-arm trials

Evaluating Bayesian adaptive randomization procedures with adaptive clip methods for multi-arm trials

Outcome-adaptive randomization in clinical trials: issues of participant welfare and autonomy

Learning with insufficient data: a multi-armed bandit perspective on covid-19 interventions

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Clinical Equipoise and Adaptive Clinical Trials

Cited by 3 publications

References 22 publications

Evaluating Bayesian adaptive randomization procedures with adaptive clip methods for multi-arm trials

Evaluating Bayesian adaptive randomization procedures with adaptive clip methods for multi-arm trials

Outcome-adaptive randomization in clinical trials: issues of participant welfare and autonomy

﻿Learning with insufficient data: a multi-armed bandit perspective on covid-19 interventions

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Learning with insufficient data: a multi-armed bandit perspective on covid-19 interventions