Statistical Analysis
Principles
This prospective meta-analysis will be carried out on studies conducted
in multiple countries, which increases effect size estimates across
different conditions as well as the external validity of the results. We
plan a prospective meta-analysis of individual de-identified
patient-level data. Common variables from all datasets will be combined
to conduct the analysis.
If consent for participation is withdrawn or consent to continue is not
given, the data will not be used unless consent to do so is obtained,
including for all mortality time points. Analyses will be performed by
intention-to-treat according to the participants’ randomly allocated
group, regardless of treatment compliance. These analyses will include
participants for whom consent to continue is refused but the use of data
already collected is allowed, including the primary outcome, and will
exclude patients who do not fulfil the study entry criteria.[8]
Missing data will not be imputed. The multilevel models described in the
analysis are able to handle missing data due to loss to follow-up. Where
there are missing observations, the number of observations used will be
reported. Two-sided hypothesis testing at a significance level of 0.05
will be used. No adjustment for multiple tests will be made, with the
interpretation of the significance of the tests being appropriate for
the primary or secondary nature of the outcome. Analyses will be
conducted using the Statistical Package for the Social Sciences (SPSS)
Research Engine, Version 24.0 IBM SPSS Statistics or later, and “R”
version 3.5.0 or later.
Monitoring and Interim analyses
We plan to perform monthly monitoring and analysis of the accumulating
data, with use of Bayesian stopping rules that allow timely decisions
without the penalties for multiple data looks and alpha spending
associated with the classic randomised controlled trial monitoring
approach. [18, 21, 22]. 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. The
prespecified stopping criteria will guide the recommendations of the
meta-trial’s executive committee. For example, if the probability of a
difference in proportions of 6% or more falls below 0.10, then the
steering committee can recommend that the meta-trial can be stopped for
futility.[9, 10]
Trial profile
Patient flow through the meta-trial will be presented in a Consolidated
Standards of Reporting Trials diagram (Figure 1).[11] We will report
the number of patients who meet the trial eligibility criteria, the
number of patients randomised, and the number of patients in the
intention-to-treat dataset for whom data are available for evaluation of
the primary outcome.
Participant characteristics and baseline comparisons
Patient characteristics at baseline will be tabulated by treatment group
(Table 3). The categorical variables will be presented as frequency
counts (n) and as a proportion of the number of patients with available
data (%). Continuous variables will be presented as summary statistics
for location (mean or median) and variability (standard deviation or
interquartile range). The total counts for variables with missing data
will be indicated.
Analyses