Detailed statistical analysis plan for a secondary Bayesian analysis of the SafeBoosC-III trial: a multinational, randomised clinical trial assessing treatment guided by cerebral oximetry monitoring versus usual care in extremely preterm infants

Abstract

Background: Extremely preterm infants have a high mortality and morbidity. Here, we present a statistical analysis plan for secondary Bayesian analyses of the pragmatic, sufficiently powered multinational, trial-SafeBoosC III-evaluating the benefits and harms of cerebral oximetry monitoring plus a treatment guideline versus usual care for such infants.

Methods: The SafeBoosC-III trial is an investigator-initiated, open-label, randomised, multinational, pragmatic, phase III clinical trial with a parallel-group design. The trial randomised 1601 infants, and the frequentist analyses were published in April 2023. The primary outcome is a dichotomous composite outcome of death or severe brain injury. The exploratory outcomes are major neonatal morbidities associated with neurodevelopmental impairment later in life: (1) bronchopulmonary dysplasia; (2) retinopathy of prematurity; (3) late-onset sepsis; (4) necrotising enterocolitis; and (5) number of major neonatal morbidities (count of bronchopulmonary dysplasia, retinopathy of prematurity, and severe brain injury). The primary Bayesian analyses will use non-informed priors including all plausible effects. The models will use a Hamiltonian Monte Carlo sampler with 1 chain, a sampling of 10,000, and at least 25,000 iterations for the burn-in period. In Bayesian statistics, such analyses are referred to as 'posteriors' and will be presented as point estimates with 95% credibility intervals (CrIs), encompassing the most probable results based on the data, model, and priors selected. The results will be presented as probability of any benefit or any harm, Bayes factor, and the probability of clinical important benefit or harm. Two statisticians will analyse the blinded data independently following this protocol.

Discussion: This statistical analysis plan presents a secondary Bayesian analysis of the SafeBoosC-III trial. The analysis and the final manuscript will be carried out and written after we publicise the primary frequentist trial report. Thus, we can interpret the findings from both the frequentists and Bayesian perspective. This approach should provide a better foundation for interpreting of our findings.

Trial registration: ClinicalTrials.org, NCT03770741. Registered on 10 December 2018.

Keywords: Bayesian statistics; Cerebral oximetry; Extremely preterm; Near-infrared spectroscopy; Randomised clinical trial; Statistical analysis plan.

Fig. 1

Presentation of a model diagnostic plot including a trace plot, histogram, autocorrelation plot, and density plot. Trace plot (upper left) should depict relative homogenous static noise without any visualisable patterns. The histograms (upper light) must depict a normal distribution. The autocorrelation plot (lower left) indicates the degree of convergence and good convergence and thereby autocorrelation becomes negligible if a pattern of decrease and ends below 0.1. The density plot (lower right) also illustrates convergence and is interpreted by estimating the similarities between the first and second half

Fig. 2

Presentation of an analysis using simulated data. The vertical black line throughout the two plots represents no difference. The upper plot presents the posterior distribution, with the blue area representing the 95% credibility interval and the blue line showing the median value. The lower plot shows the cumulative posterior distribution