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Background: We aimed to design a Bayesian adaption trial through extensivesimulations to determine values for key design parameters, demonstrate errorrates, and establish the expected sample size. The complexity of the proposedoutcome and analysis meant that Markov Chain Monte Carlo methods were required,resulting in an infeasible computational burden. Thus, we leveraged theIntegrated Nested Laplace Approximations (INLA) algorithm, a fast approximationmethod, to ensure the feasibility of these simulations. Methods: We simulatedBayesian adaptive two-arm superiority trials that stratified participants intotwo disease severity states. The outcome was analyzed with proportional oddslogistic regression. Trials were stopped for superiority or futility,separately for each state. We calculated the type I error and power across 64scenarios that varied the stopping thresholds and the minimum sample sizebefore commencing adaptive analyses. We incorporated dynamic borrowing and usedINLA to compute the posterior distributions at each adaptive analysis. Designsthat maintained a type I error below 5%, a power above 80%, and a feasible meansample size were then evaluated across 22 scenarios that varied the odds ratiosfor the two severity states. Results: Power generally increased as the initialsample size and the threshold for declaring futility increased. Two designswere selected for further analysis. In the comprehensive simulations, the onedesign had a higher chance of reaching a trial conclusion before the maximumsample size and higher probability of declaring superiority when appropriatewithout a substantial increase in sample size for the more realistic scenariosand was selected as the trial design. Conclusions: We designed a Bayesianadaptive trial to evaluate novel strategies for ventilation using the INLAalgorithm to and optimize the trial design through simulation.

Designing a Bayesian adaptive clinical trial to evaluate novel  mechanical ventilation strategies in acute respiratory failure using  Integrated Nested Laplace Approximations