Overcoming underpowering: Trial simulations and a global rank end point to optimize clinical trials in children with heart disease.

Background: Randomized controlled trials (RCTs) in children with heart disease are challenging and therefore infrequently performed. We sought to improve feasibility of perioperative RCTs for this patient cohort using data from a large, multicenter clinical registry. We evaluated potential enrollment and end point frequencies for various inclusion cohorts and developed a novel global rank trial end point. We then performed trial simulations to evaluate power gains with the global rank end point and with use of planned covariate adjustment as an analytic strategy.

Methods: Data from the Society of Thoracic Surgery-Congenital Heart Surgery Database (STS-CHSD, 2011-2016) were used to support development of a consensus-based global rank end point and for trial simulations. For Monte Carlo trial simulations (n = 50,000/outcome), we varied the odds of outcomes for treatment versus placebo and evaluated power based on the proportion of trial data sets with a significant outcome (P < .05).

Results: The STS-CHSD study cohort included 35,967 infant index cardiopulmonary bypass operations from 103 STS-CHSD centers, including 11,411 (32%) neonatal cases and 12,243 (34%) high-complexity (Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery mortality category ≥4) cases. In trial simulations, study power was 21% for a mortality-only end point, 47% for a morbidity and mortality composite, and 78% for the global rank end point. With covariate adjustment, power increased to 94%. Planned covariate adjustment was preferable to restricting to higher-risk cohorts despite higher event rates in these cohorts.

Conclusions: Trial simulations can inform trial design. Our findings, including the newly developed global rank end point, may be informative for future perioperative trials in children with heart disease.