Handling Poor Accrual in Pediatric Trials

In the conduction of trials, a common situation is related to potential difficulties in recruiting the planned sample size as provided by the study design. A Bayesian analysis of such trials might provide a framework to combine prior evidence with current evidence, and it is an accepted approach by regulatory agencies. However, especially for small trials, the Bayesian inference may be severely conditioned by the prior choices. The Renal Scarring Urinary Infection (RESCUE) trial, a pediatric trial that was a candidate for early termination due to underrecruitment, served as a motivating example to investigate the effects of the prior choices on small trial inference. The trial outcomes were simulated by assuming 50 scenarios combining different sample sizes and true absolute risk reduction (ARR). The simulated data were analyzed via the Bayesian approach using 0%, 50%, and 100% discounting factors on the beta power prior. An informative inference (0% discounting) on small samples could generate data-insensitive results. Instead, the 50% discounting factor ensured that the probability of confirming the trial outcome was higher than 80%, but only for an ARR higher than 0.17. A suitable option to maintain data relevant to the trial inference is to define a discounting factor based on the prior parameters. Nevertheless, a sensitivity analysis of the prior choices is highly recommended

Bayesian inference · Bayesian probability · Clinical trial · Discounting · Econometrics · Economics · Inference · Randomized controlled trial · Sample size determination · Statistics · Advanced Causal Inference Techniques · Computer Science · Health Systems, Economic Evaluations, Quality of Life · Mathematics · Medicine · Statistical Methods in Clinical Trials · Artificial Intelligence · Internal Medicine

• On Using Bayesian Methods to Address Small Sample Problems

  Daniel McNeish•Structural Equation Modeling: A…•2016

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