Abstract
Background
Personalized medicine, driven by genomic insights, has catalyzed the emergence of innovative clinical trial designs such as basket and umbrella trials. These designs are particularly suited for evaluating targeted therapies in biomarker-defined subgroups and rare pediatric conditions where traditional trials face challenges of small sample sizes and disease heterogeneity.Objectives
This systematic review aimed to characterize and synthesize the current literature on basket and umbrella trial designs in pediatric drug development, with a focus on their methodological, regulatory, and statistical aspects.Methods
A systematic search was conducted in the electronic databases PubMed, Scopus, and Web of Science to identify all literature related to basket and umbrella trials. A text mining analysis using unsupervised machine learning technique was performed with relevant articles to automatically identify the primary topics within publications on basket and umbrella trials. A systematic search of PubMed, Scopus, and Web of Science identified 1867 records. After screening and eligibility assessment, 28 studies were included in the final review. Topic modelling using Latent Dirichlet Allocation (LDA) was performed on 76 pertinent articles to identify dominant themes. Statistical convergence, topic coherence, and classification accuracy (> 85%) were validated.Results
A systematic search of PubMed, Scopus, and Web of Science identified 1867 records. After screening and eligibility assessment, 28 studies were included in the final review. Basket trial designs were more prevalent than umbrella trials, particularly in early-phase oncology and rare disease research. Topic modelling using Latent Dirichlet Allocation (LDA) was performed on 76 relevant articles to identify dominant themes. Statistical convergence, topic coherence, and classification accuracy (> 85%) were validated.Conclusions
Basket and umbrella trials offer substantial advantages for pediatric drug development by increasing trial efficiency, enabling precision targeting, and supporting adaptive decision-making. Their success depends on robust statistical planning, careful use of Bayesian methods, and attention to regulatory guidance.