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

Abstract

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.

Methods: Rather than reducing the outcome to binary endpoints such as intubation, we modeled the complete distribution of mechanical ventilation duration using Bayesian models specifically designed for the underlying data structure, including Hurdle, Zero-One Inflated Beta (ZOIB), Bayesian linear, and cumulative logistic regression models. These approaches accommodate excess zeros, skewness, bounded outcomes, and ordinal representations while naturally incorporating uncertainty through posterior probability distributions. Predictive performance was evaluated using Leave-One-Out crossvalidation.

Results: Advanced Bayesian models provided substantially better characterization of the observed outcome distribution than conventional Gaussian models. The ZOIB model demonstrated the best predictive performance, accurately representing both the probability of requiring invasive ventilation and the distribution of ventilation duration among treated patients. Hurdle and cumulative logistic models similarly captured clinically relevant aspects of the outcome while preserving interpretability. Across all Bayesian analyses, HFNC remained noninferior to NIV regarding intubation risk, with a lower expected duration of mechanical ventilation and high posterior probability of noninferiority.

Conclusions: The choice of statistical model should be driven by the biological and clinical characteristics of the endpoint rather than analytical convenience. Bayesian models tailored to semi-continuous, zero-inflated, and bounded outcomes provide richer clinical interpretation, avoid the loss of information associated with dichotomization, and support probability-based decision making. This framework is broadly applicable to pediatric critical care and other clinical trials involving complex composite or duration-based outcomes, offering a principled approach for improving treatment effect estimation and clinical inference.