Optimizing Pediatric Outcomes: Bayesian Modeling of Complex Ventilation-Free Outcomes in Pediatric Respiratory Trials

Introduction

Clinical trials in pediatric intensive care increasingly rely on complex endpoints, such as duration of mechanical ventilation or ventilation-free days, which are bounded, highly skewed, and characterized by a large proportion of structural zeros. Conventional statistical approaches frequently assume normality or simplify these outcomes through dichotomization, resulting in substantial information loss and potentially masking clinically meaningful treatment effects. This work illustrates a Bayesian framework for modeling complex respiratory outcomes using data from a randomized noninferiority trial comparing high-flow nasal cannula (HFNC) and non-invasive ventilation (NIV) in 252 infants hospitalized with bronchiolitis Objectives: To evaluate Bayesian models tailored to semi-continuous, zero-inflated, and bounded respiratory outcomes and compare their predictive performance with conventional approaches.