Contemporary strategies to improve clinical trial design for critical care research: insights from the First Critical Care Clinical Trialists Workshop

Michael O Harhay, Jonathan D Casey, Marina Clement, Sean P Collins, Étienne Gayat, Michelle Ng Gong, Samir Jaber, Pierre-François Laterre, John C Marshall, Michael A Matthay, Rhonda E Monroe, Todd W Rice, Eileen Rubin, Wesley H Self, Alexandre Mebazaa, Michael O Harhay, Jonathan D Casey, Marina Clement, Sean P Collins, Étienne Gayat, Michelle Ng Gong, Samir Jaber, Pierre-François Laterre, John C Marshall, Michael A Matthay, Rhonda E Monroe, Todd W Rice, Eileen Rubin, Wesley H Self, Alexandre Mebazaa

Abstract

Background: Conducting research in critically-ill patient populations is challenging, and most randomized trials of critically-ill patients have not achieved pre-specified statistical thresholds to conclude that the intervention being investigated was beneficial.

Methods: In 2019, a diverse group of patient representatives, regulators from the USA and European Union, federal grant managers, industry representatives, clinical trialists, epidemiologists, and clinicians convened the First Critical Care Clinical Trialists (3CT) Workshop to discuss challenges and opportunities in conducting and assessing critical care trials. Herein, we present the advantages and disadvantages of available methodologies for clinical trial design, conduct, and analysis, and a series of recommendations to potentially improve future trials in critical care.

Conclusion: The 3CT Workshop participants identified opportunities to improve critical care trials using strategies to optimize sample size calculations, account for patient and disease heterogeneity, increase the efficiency of trial conduct, maximize the use of trial data, and to refine and standardize the collection of patient-centered and patient-informed outcome measures beyond mortality.

Keywords: Acute respiratory distress syndrome; Clinical trials; Critical care; Sepsis.

Conflict of interest statement

Dr. Jaber reports receiving consulting fees from Drager, Fisher & Paykel, Medtronic, Baxter, and Fresenius-Xenios. Dr. Laterre reports personal fees from Adrenomed, Ferring, and Inotrem. Dr. Mebazaa reports personal fees from Novartis, Orion, Roche, Servier, Sanofi, Otsuka, Philips, grants and personal fees from Adrenomed, Abbott, and grants from 4TEEN4. On behalf of all authors, the corresponding author states that there are no additional potential conflicts of interest.

Figures

Fig. 1
Fig. 1
Visual depiction of key design elements and differences in randomized trial design
Fig. 2
Fig. 2
Depiction of a hypothetical Bayesian trial analysis using four different prior probability distributions. To conduct a Bayesian trial analysis, researchers must first select (ideally a priori) “priors,” (shown in red in the figure). Priors are meant to reflect the range of potential effect distributions that are expected before starting a trial. These priors are combined with the observed treatment effect in the trial (referred to as the likelihood function; indicated by the light brown shading). A Bayesian trial analysis typically uses a range of prior distributions, but the likelihood function is constant as it reflects the actual data observed in the trial. Each prior distribution is statistically combined with the likelihood function to create the Bayesian treatment effect estimate (i.e., posterior probability) distribution (outlined in black). As shown, the Bayesian treatment effect estimates may vary considerably based on the selected prior. There are several possible methods for choosing priors including (i) using results of observational studies, trials, or meta-analyses, (ii) eliciting expert opinion, or (iii) using a range of hypothetical distributions that assume very skeptical to enthusiastic effect distributions. The selection of priors is a critically important step in Bayesian trial analysis, and interested readers are directed to a recent technical tutorial [64], and the Bayesian re-analyses of the EOLIA [67] and ANDROMEDA-SHOCK trials [68] for guidance on prior selection and execution of a Bayesian analysis

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