Paper
A manifesto for the future of ICU trials
Published Dec 1, 2020 · E. Goligher, F. Zampieri, C. Calfee
Critical Care
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Abstract
© The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creat iveco mmons .org/publi cdoma in/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. The intensive care unit (ICU) is both a challenging and opportune environment for the conduct of clinical trials. On the one hand, competing determinants of patient outcome (including multi-morbidity and pre-ICU illness trajectory) and the heterogeneity of critical illness syndromes attenuate the population-average treatment effect [1, 2]. On the other hand, the ICU is a controlled environment that facilitates monitoring of protocol adherence and outcome ascertainment. ICU trials may be improperly powered because of overly optimistic assumptions about the baseline event rate in the control group and about the predicted effect of treatment on that event rate [3, 4]. The treatment effect required to demonstrate statistically significant benefit often substantially exceeds what might be considered the minimum clinically relevant benefit, and consequently, trials sometimes are interpreted to show “no evidence of benefit” even when clinically relevant benefits are observed. The COVID-19 pandemic has shown that we need to (and can) find a way to deliver more effectively on trials in the ICU. The benefit of dexamethasone was demonstrated within just a few short months of the outbreak of the global pandemic [5]. Conversely, many tens of thousands of patients were treated with unproven and potentially harmful therapies outside of trials, and the benefit of certain interventions remains uncertain due to the challenges of completing trials of these rapidly adopted therapies. We therefore propose a manifesto for the future of ICU trials (Table 1). 1 Think Bayesian Bayesian analysis is an alternate statistical paradigm that answers the question “what is the probability of treatment effect” in contrast to the traditional frequentist approach, which answers the question “what is the probability of these data, assuming no treatment effect?” Under the Bayesian framework, trial information is not biased by “looking at” the data, and the results can be continuously re-estimated and updated as additional information (i.e., patient outcomes) is added to the dataset [6]. To put it simply (and perhaps somewhat simplistically), conventional frequentist statistics views the entire trial as a single “coin flip”; technically, there is no information to draw conclusions until the trial is completed. By contrast, Bayesian statistics regards each individual patient’s outcome as a “coin flip”; the estimated probability of benefit or harm can be continuously updated as information accumulates. We contend that the Bayesian approach is ideal because it (a) directly answers the questions of interest (probabilities of clinically relevant benefit, harm, or futility), thereby reducing the risk of a false “positive” or false “negative” conclusion; and (b) the continuously updated posterior permits maximally efficient trial adaptations in sample size and treatment allocation [7]. 2 Adapt when needed Most trials in COVID-19 adopted an adaptive trial design given deep uncertainty about actual event rates and treatment effects. Adaptive designs respond flexibly to observed event rates and treatment effect, avoiding the risk of underestimating sample size requirement because of overly optimistic predictions about event rates and treatment effect [8]. Adapting treatment allocation probabilities within the randomization algorithm (response-adaptive randomization) can also increase trial efficiency in trials with three or more arms by Open Access
ICU trials should adopt a Bayesian approach to better understand treatment effects and minimize biases, ensuring more effective treatment decisions for critically ill patients.
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