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.

Clinical trial design for rare diseases can be challenging due to limited data, heterogeneous clinical manifestations and progression, and a frequent lack of adequate knowledge about the disease. Multiple endpoints are usually used to collectively assess the effectiveness of the investigational drug on multiple aspects of the disease. Here we propose an adaptive design based on the promising zone framework, allowing for sample size re-estimation (SSR) using interim data for a clinical trial involving multiple endpoints. The proposed SSR procedure incorporates two global tests: the ordinary least squares (OLS) test and the nonparametric permutation test. We consider two SSR approaches: one is based on power (SSR-Power) and the other on conditional power (SSR-CP). Simulation results show that the adaptive design achieves type I error control and satisfactory power. Compared with the permutation test, the OLS test has improved type I error control when the sample size is small and the timing of the interim analysis is early; while the permutation test achieves slightly higher power in most scenarios. Regarding the SSR methods, SSR-CP consistently achieves higher power than SSR-Power but often requires a larger sample size and more frequently reaches the maximum allowable sample size. The proposed design is particularly useful when the trial has a small initial sample size and has opportunity to adjust the sample size at an interim analysis to achieve adequate power.