Clinical trials in critical care: can a Bayesian approach enhance clinical and scientific decision making?

Yarnell, Christopher J.; Abrams, Darryl; Baldwin, Matthew R.; Brodie, Daniel; Fan, Eddy; Ferguson, Niall D.; Hua, May; Madahar, Purnema; McAuley, Danny; Munshi, Laveena; Perkins, Gavin D; Rubenfeld, Gordon D.; Slutsky, Arthur S.; Wunsch, Hannah; Fowler, Robert; Tomlinson, George; Beitler, Jeremy R.; Goligher, Ewan C.

doi:10.1016/s2213-2600(20)30471-9

Cited by 62 publications

(59 citation statements)

References 57 publications

(72 reference statements)

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“…52 The analytical strategy-ie, the use of a Bayesian design, as in REMAP-CAP, 26 versus frequentist approaches-might be another source of observed discrepancies. 53 For example, the use of adaptive platforms within a Bayesian framework could maximise the finding and interpretation of a beneficial effect. Furthermore, the choice of priors and stopping rules affect the decision to stop enrolment (or not) and will thus change the final estimated effect of a given trial.…”

Section: Outcome Selection and Analytical Strategymentioning

confidence: 99%

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Interleukin-6 receptor blockade in patients with COVID-19: placing clinical trials into context

Angriman

Ferreyro

Burry

et al. 2021

The Lancet Respiratory Medicine

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Section: Outcome Selection and Analytical Strategymentioning

confidence: 99%

“…Importantly, it has been shown that the conclusions from frequentist and Bayesian analysis of the same evidence are not always consistent. 53…”

Section: Outcome Selection and Analytical Strategymentioning

confidence: 99%

Interleukin-6 receptor blockade in patients with COVID-19: placing clinical trials into context

Angriman

Ferreyro

Burry

et al. 2021

The Lancet Respiratory Medicine

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“…A Bayesian reanalysis can be a helpful tool to augment the interpretation of critical care trials ( 8 , 17 , 18 , 33 – 37 ). In this review, we have highlighted potential challenges with the interpretation of results from a trial conducted using frequentist methods.…”

Section: Discussionmentioning

confidence: 99%

“…The primary controversy stems from confusion and frustration regarding the meaning (and thus the interpretation) of P values and null hypothesis testing, which are statistical concepts employed to design and analyze trials in an approach to statistics known as frequentism ( 1 ). One consequence of this debate regarding RCTs is a surging interest in Bayesian statistical thinking, which can be used to design, analyze, and interpret new clinical trials or to reanalyze trials reported using frequentist methods in an attempt to contextualize the results ( 2 – 8 ).…”

mentioning

confidence: 99%

Using Bayesian Methods to Augment the Interpretation of Critical Care Trials. An Overview of Theory and Example Reanalysis of the Alveolar Recruitment for Acute Respiratory Distress Syndrome Trial

Zampieri

Casey

Shankar-Hari

et al. 2021

Am J Respir Crit Care Med

101

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Most randomized trials are designed and analyzed using frequentist statistical approaches such as null-hypothesis testing and P-values. Conceptually, P-values are cumbersome to understand as they provide evidence of data incompatibility with a null hypothesis (e.g., no clinical benefit) and not direct evidence of the alternative hypothesis (e.g., clinical benefit). This counterintuitive framework may contribute to the misinterpretation that the absence of evidence is equal to evidence of absence and may cause the discounting of potentially informative data. Bayesian methods provide an alternative, probabilistic interpretation of data. The reanalysis of completed trials using Bayesian methods is becoming increasingly common, particularly for trials with effect estimates that appear clinically significant despite P-values above the traditional threshold of 0.05. Statistical inference using Bayesian methods produces a distribution of effect sizes that would be compatible with observed trial data, interpreted in the context of prior assumptions about an intervention (called 'priors'). These 'priors' are chosen by investigators to reflect existing beliefs and past empirical evidence regarding the effect of an intervention. By calculating the likelihood of clinical benefit, a Bayesian reanalysis can augment the interpretation of a trial. However, if priors are not defined a priori, there is a legitimate concern that priors could be constructed in a manner that produces biased results. Therefore, some standardization of priors for Bayesian reanalysis of clinical trials may be desirable for the critical care community. In this Critical Care Perspective, we discuss both frequentist and Bayesian approaches to clinical trial analysis, introduce a framework that researchers can use to select priors for a Bayesian reanalysis, and demonstrate how to apply our proposal by conducting a novel Bayesian trial reanalysis.

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“…These designs may be more efficient than traditional trial designs for identifying patients who benefit in settings in which HTE exists within the study population. Frequentist or Bayesian statistical approaches may be used in these designs depending on the complexity of potential modifications ( 35 , 36 ). The additional interim analyses and modifications potentially increase the risk of false positive results.…”

Section: Recommendationsmentioning

confidence: 99%

A Research Agenda for Precision Medicine in Sepsis and Acute Respiratory Distress Syndrome: An Official American Thoracic Society Research Statement

Shah¹,

Meyer²,

Angus³

et al. 2021

Am J Respir Crit Care Med

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Background: Precision medicine focuses on the identification of therapeutic strategies that are effective for a group of patients based on similar unifying characteristics. The recent success of precision medicine in non–critical care settings has resulted from the confluence of large clinical and biospecimen repositories, innovative bioinformatics, and novel trial designs. Similar advances for precision medicine in sepsis and in the acute respiratory distress syndrome (ARDS) are possible but will require further investigation and significant investment in infrastructure. Methods: This project was funded by the American Thoracic Society Board of Directors. A multidisciplinary and diverse working group reviewed the available literature, established a conceptual framework, and iteratively developed recommendations for the Precision Medicine Research Agenda for Sepsis and ARDS. Results: The following six priority recommendations were developed by the working group: 1 ) the creation of large richly phenotyped and harmonized knowledge networks of clinical, imaging, and multianalyte molecular data for sepsis and ARDS; 2 ) the implementation of novel trial designs, including adaptive designs, and embedding trial procedures in the electronic health record; 3 ) continued innovation in the data science and engineering methods required to identify heterogeneity of treatment effect; 4 ) further development of the tools necessary for the real-time application of precision medicine approaches; 5 ) work to ensure that precision medicine strategies are applicable and available to a broad range of patients varying across differing racial, ethnic, socioeconomic, and demographic groups; and 6 ) the securement and maintenance of adequate and sustainable funding for precision medicine efforts. Conclusions: Precision medicine approaches that incorporate variability in genomic, biologic, and environmental factors may provide a path forward for better individualizing the delivery of therapies and improving care for patients with sepsis and ARDS.

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Clinical trials in critical care: can a Bayesian approach enhance clinical and scientific decision making?

Abstract: Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 62 publications

References 57 publications

Interleukin-6 receptor blockade in patients with COVID-19: placing clinical trials into context

Interleukin-6 receptor blockade in patients with COVID-19: placing clinical trials into context

Using Bayesian Methods to Augment the Interpretation of Critical Care Trials. An Overview of Theory and Example Reanalysis of the Alveolar Recruitment for Acute Respiratory Distress Syndrome Trial

A Research Agenda for Precision Medicine in Sepsis and Acute Respiratory Distress Syndrome: An Official American Thoracic Society Research Statement

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