Monitoring and Interim analyses
We plan to perform monthly monitoring and analysis of the primary outcome in the accumulating data, with use of Bayesian monitoring rules that allow timely decisions without the penalties for multiple data looks and alpha spending associated with the classic randomised controlled trial monitoring approach. [21, 30, 31] At the first interim analysis, the prior distribution of the proportion of patients intubated will be multiplied by the likelihood of the observed data to give a posterior distribution of the proportion of patients intubated. At each subsequent interim analysis, the previous posterior distribution becomes the new prior, and a new posterior distribution of the proportion of patients who were intubated will be reported. The pooling of data into the prior distributions and the Bayesian updating of posterior distributions prevent the stopping rule from being overly influenced by potential bias from differential recruitment rates in different trials. Prespecified monitoring criteria will guide the recommendations of the meta-trial’s executive committee. If the probability of a difference in proportions of intubated patients in the two groups of 6% or more rises above 0.90, then the executive committee can recommend that interim analyses be conducted following the methods in the analyses section, to support a decision to stop the meta-trial for efficacy. If the probability of a difference in proportions of 6% or more falls below 0.10, then the executive committee can recommend that interim analyses be conducted following the methods in the analyses section, to support a decision to stop the meta-trial for futility.[31, 32]