Table 1.

Recommendations to improve clinical trial design for critical care research from the First Critical Care Clinical Trialists (3CT) Workshop

Domain	Recommendation	Description and comment
Study design	Pre-specify plans for sample size re-estimation during trial design	Allows for the adjustment of the targeted sample size if outcome event rates observed in the trial differ from the initial power calculation
	Use predictive enrichment strategies for interventions in which there is a mechanistic rationale (physiologic, biologic, or genetic) to suggest why some patients may respond while others do not	Uses data from prior trials or observational data to identify patients who are likely to experience the most benefit from a given intervention, with the goal of developing enrollment criteria to selectively enroll these patients
	Use pragmatic trials to evaluate supportive therapies that might benefit a wide range of conditions or patients (e.g., early mobilization, ventilator weaning strategies, types of fluid resuscitation)	Uses broad enrollment criteria to enroll a diverse group of patients that are representative of those who would receive the intervention in usual care
	Use response-adaptive randomization for early phase trials and trials evaluating conditions with many available treatments	Incorporates information learned during the trial to (i) optimize allocation to study arms yielding the best results, which minimizes risks to patients; or (ii) optimize enrollment criteria enriching for better performing subgroups
	Evaluate opportunities to incorporate multiple trial interventions into platform trials	Simultaneously randomizes multiple, independent interventions or intervenes at multiple points in the same disease process (e.g., a trial evaluating initial therapy for a condition that feeds directly into a second trial of rescue therapies)
Study design and analysis	Incorporate a pre-specified Bayesian analysis plan with a range of priors	Analyzes trial results in the context of previously observed or presumed treatment effect distributions, producing results in terms of a likelihood of an effect on a probabilistic scale (i.e., the probability of an effect being present on a scale of 0–100)
Study conduct	Improve collaboration between critical care and pre-ICU providers (emergency medicine, pre-hospital)	Allows intervention earlier in the course of critical illness and significantly improves enrollment for interventions with narrow therapeutic windows
Outcome measures	Attempt to standardize common outcome measures across trials	Allows for meaningful across-trial comparisons
	Integrate diverse stakeholders (such as patients and families) into trial design and continue research on the development, measurement, and timing of patient-reported outcome measures	Promotes patient-centered critical care, while addressing the key challenges of patient-reported outcome measures, including the ideal timing of collection, how to account for the competing risk of mortality, and the possibility of biases introduced by incomplete long-term follow-up
Data Sharing	Encourage data sharing of de-identified patient data	Sharing data with robust data dictionaries to investigators who have pre-specified secondary analyses provides opportunities to maximize the knowledge gained from clinical trials and maximally leverages the investments made by patients, funding organizations, and researchers