The New England Journal of Statistics in Data Science

The MiniPDX model is a cost-effective preclinical platform that replicates human tumor microenvironments in mice, enabling rapid drug response evaluation in precision oncology. In contrast, the patient-derived xenograft (PDX) model offers a more comprehensive but slower and costlier approach by engrafting human tumors into immunodeficient mice to evaluate efficacy in a physiologically relevant context. In this study, we present an integrated Bayesian framework that supports both separate and joint analyses of MiniPDX and PDX data. Our models borrow information across biomarker-defined subgroups and between assays, improving the efficiency and precision of drug effect estimation. Furthermore, we implement an adaptive sample size re-estimation procedure for MiniPDX experiments, allowing for more flexible designs and optimized resource utilization. Together, these methods provide a Bayesian framework that improves estimation efficiency for small-sample MiniPDX and PDX experiments and offers a principled adaptive sample-size rule, helping to identify biomarker-defined subpopulations with distinct predictive profiles for treatment response.