JAMA pediatrics · 2024
Whole-of-Life Inclusion in Bayesian Adaptive Platform Clinical Trials.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Campbell Anita J, Anpalagan Keerthi, Best Emma J, Britton Philip N, Gwee Amanda, Hatcher James, Manley Brett J, Marsh Julie, Webb Rachel H, Davis Joshua S, Mahar Robert K, McGlothlin Anna, McMullan Brendan, Meyer Michael, Mora Jocelyn, Murthy Srinivas, Nourse Clare, Papenburg Jesse, Schwartz Kevin L, Scheuerman Oded, Snelling Thomas, Strunk Tobias, Stark Michael, Voss Lesley, Tong Steven Y C, Bowen Asha C, Staphylococcus aureus Network Adaptive Platform Paediatric and Youth (SNAP-PY) working groupSNAP Global Trial Steering Committee
Аннотация
IMPORTANCE: There is a recognized unmet need for clinical trials to provide evidence-informed care for infants, children and adolescents. This Special Communication outlines the capacity of 3 distinct trial design strategies, sequential, parallel, and a unified adult-pediatric bayesian adaptive design, to incorporate children into clinical trials and transform this current state of evidence inequity. A unified adult-pediatric whole-of-life clinical trial is demonstrated through the Staphylococcus aureus Network Adaptive Platform (SNAP) trial.
OBSERVATIONS: Bayesian methods provide a framework for synthesizing data in the form of a probability model that can be used in the design and analysis of a clinical trial. Three trial design strategies are compared: (1) a sequential adult-pediatric bayesian approach that involves a separate, deferred pediatric trial that incorporates existing adult trial data into the analysis model to potentially reduce the pediatric trial sample size; (2) a parallel adult-pediatric bayesian trial whereby separate pediatric enrollment occurs in a parallel trial, running alongside an adult randomized clinical trial; and (3) a unified adult-pediatric bayesian adaptive design that supports the enrollment of both children and adults simultaneously in a whole-of-life bayesian adaptive randomized clinical trial. The SNAP trial whole-of-life design uses a bayesian hierarchical model that allows information sharing (also known as borrowing) between trial age groups by linking intervention effects of children and adults, thereby improving inference in both groups.
CONCLUSION AND RELEVANCE: Bayesian hierarchical models may provide more precision for estimates of safety and efficacy of treatments in trials with heterogenous populations compared to traditional methods of analysis. They facilitate the inclusion of children in clinical trials and a shift from children deemed therapeutic orphans to the vision of no child left behind in clinical trials to ensure evidence for clinical practice exists across the life course. The SNAP trial provides an example of a bayesian adaptive whole-of-life inclusion design that enhances trial population inclusivity and diversity overall, as well as generalizability and translation of findings into clinical practice.
OBSERVATIONS: Bayesian methods provide a framework for synthesizing data in the form of a probability model that can be used in the design and analysis of a clinical trial. Three trial design strategies are compared: (1) a sequential adult-pediatric bayesian approach that involves a separate, deferred pediatric trial that incorporates existing adult trial data into the analysis model to potentially reduce the pediatric trial sample size; (2) a parallel adult-pediatric bayesian trial whereby separate pediatric enrollment occurs in a parallel trial, running alongside an adult randomized clinical trial; and (3) a unified adult-pediatric bayesian adaptive design that supports the enrollment of both children and adults simultaneously in a whole-of-life bayesian adaptive randomized clinical trial. The SNAP trial whole-of-life design uses a bayesian hierarchical model that allows information sharing (also known as borrowing) between trial age groups by linking intervention effects of children and adults, thereby improving inference in both groups.
CONCLUSION AND RELEVANCE: Bayesian hierarchical models may provide more precision for estimates of safety and efficacy of treatments in trials with heterogenous populations compared to traditional methods of analysis. They facilitate the inclusion of children in clinical trials and a shift from children deemed therapeutic orphans to the vision of no child left behind in clinical trials to ensure evidence for clinical practice exists across the life course. The SNAP trial provides an example of a bayesian adaptive whole-of-life inclusion design that enhances trial population inclusivity and diversity overall, as well as generalizability and translation of findings into clinical practice.