Andrew J. Martin | Searcharxiv

arXiv2026

COBRA-DOSE: Copula-based Bayesian Model Averaging for Dose Selection

Early-phase clinical trials for dose selection typically enrol few patients and aim to identify doses that are both safe and promising for further study. While traditional approaches identify the maximum tolerated dose, modern trials for targeted therapies often seek the optimal biological dose, defined as the lowest dose achieving sufficient biological activity with acceptable safety. In immunology settings, assessment of biological activity is based on multiple biomarkers or clinical endpoints. Clinicians leading dose-selection efforts would thus benefit from transparent summaries of the probabilities of observing combinations of biomarker outcomes across doses. However, such inference is challenging in small samples where complex modelling assumptions are difficult to verify. To address this limitation, we propose COBRA-DOSE, a framework for posterior predictive inference based on two endpoints that models dependence via copulas and accounts for uncertainty in both marginal distributions and dependence structures through Bayesian model averaging. This approach avoids reliance on a single model and yields interpretable quantities for clinical decision making. We demonstrate the performance of COBRA-DOSE using DEN-181, a phase I immunology trial in rheumatoid arthritis. We also provide a general implementation of our approach through the CobraDose package in R.

stat.ME↗

arXiv2025

An Efficient Framework for Robust Sample Size Determination

In many settings, robust data analysis involves computational methods for uncertainty quantification and statistical inference. To design frequentist studies that leverage robust analysis methods, suitable sample sizes to achieve desired power are often found by estimating sampling distributions of p-values via intensive simulation. Moreover, most sample size recommendations rely heavily on assumptions about a single data-generating process. Consequently, robustness in data analysis does not by itself imply robustness in study design, as examining sample size sensitivity to data-generating assumptions typically requires further simulations. We propose an economical alternative for determining sample sizes that are robust to multiple data-generating mechanisms. Applying our theoretical results that model p-values as a function of the sample size, we assess power across the sample size space using simulations conducted at only two sample sizes for each data-generating mechanism. We demonstrate the broad applicability of our methodology to study design based on M-estimators in both experimental and observational settings through a varied set of clinical examples.

stat.ME↗