Designing a Bayesian adaptive clinical trial to evaluate novel mechanical ventilation strategies in acute respiratory failure using integrated nested Laplace approximations

Reyhaneh Hosseini; Ziming Chen; Ewan Goligher; Eddy Fan; Niall D Ferguson; Michael O Harhay; Sarina Sahetya; Martin Urner; Christopher J Yarnell; Anna Heath

doi:10.1016/j.cct.2024.107560

Designing a Bayesian adaptive clinical trial to evaluate novel mechanical ventilation strategies in acute respiratory failure using integrated nested Laplace approximations

Contemp Clin Trials. 2024 May 10:142:107560. doi: 10.1016/j.cct.2024.107560. Online ahead of print.

Authors

Affiliations

¹ Child Health Evaluative Sciences, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, ON, Canada.
² Department of Medicine, Division of Respirology, University Health Network, Toronto, ON, Canada.
³ Department of Medicine, Division of Respirology, University Health Network, Toronto, ON, Canada; Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada; Insititute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Canada.
⁴ Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
⁵ Division of Pulmonary and Critical Care Medicine, Johns Hopkins Hospital, Baltimore, MD, USA.
⁶ Department of Anesthesiology and Pain Medicine, University of Toronto, Toronto, Canada; Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada.
⁷ Department of Medicine, Division of Respirology, University Health Network, Toronto, ON, Canada; Insititute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Canada.
⁸ Child Health Evaluative Sciences, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, ON, Canada; Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; Department of Statistical Science, University College London, London, UK. Electronic address: anna.heath@sickkids.ca.

PMID: 38735571
DOI: 10.1016/j.cct.2024.107560

Abstract

Background: Adaptive trials usually require simulations to determine values for design parameters, demonstrate error rates, and establish the sample size. We designed a Bayesian adaptive trial comparing ventilation strategies for patients with acute hypoxemic respiratory failure using simulations. The complexity of the analysis would usually require computationally expensive Markov Chain Monte Carlo methods but this barrier to simulation was overcome using the Integrated Nested Laplace Approximations (INLA) algorithm to provide fast, approximate Bayesian inference.

Methods: We simulated two-arm Bayesian adaptive trials with equal randomization that stratified participants into two disease severity states. The analysis used a proportional odds model, fit using INLA. Trials were stopped based on pre-specified posterior probability thresholds for superiority or futility, separately for each state. We calculated the type I error and power across 64 scenarios that varied the probability thresholds and the initial minimum sample size before commencing adaptive analyses. Two designs that maintained a type I error below 5%, a power above 80%, and a feasible mean sample size were evaluated further to determine the optimal design.

Results: Power generally increased as the initial sample size and the futility threshold increased. The chosen design had an initial recruitment of 500 and a superiority threshold of 0.9925, and futility threshold of 0.95. It maintained high power and was likely to reach a conclusion before exceeding a feasible sample size.

Conclusions: We designed a Bayesian adaptive trial to evaluate novel strategies for ventilation using the INLA algorithm to efficiently evaluate a wide range of designs through simulation.

Keywords: Acute hypoxemic respiratory failure; Bayesian clinical trial design; Critical care medicine; Integrated nested Laplace approximations; Proportional odds model.