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]