arXiv subjects

Huiman X. Barnhart

Publications and source records attributed to Huiman X. Barnhart.

2 recordsLinked to original sources

arXiv2026

The FORSS Framework for Sample Size and Power Calculations With Win Statistics for Hierarchical Endpoints

Win statistics have gained increasing popularity as primary analysis methods for clinical trials with hierarchical endpoints (HEs) as primary endpoints. However, existing sample size and power calculation approaches in trial design still face several limitations and challenges: simulation-based approaches are computationally intensive, while existing formula-based methods often rely on simplifying assumptions such as independence among HEs, or require specification of overall win statistics and tie probability that are difficult to elicit a priori in practice. To address these challenges, we propose the FORSS framework, a FORmula-based Super-Sample approach that allows investigators to specify marginal treatment effects using familiar metrics (e.g., hazard ratios, mean differences, and risk differences) together with a flexible joint working distribution for the HEs. Rather than repeatedly simulating full trials at each candidate sample size, FORSS uses super-samples to estimate the population-level plug-in quantities required by analytical formulas for both power and sample size calculation. We evaluated the performance of the proposed FORSS through extensive simulation studies. The results show that the formula-based FORSS closely matches empirical power across a wide range of scenarios while maintaining Type~I error rates near the nominal 5\% level. An illustration based on the HEART-FID trial further shows that endpoint-dependence specifications can materially affect projected power and required sample size when planning trials with HEs.

stat.ME↗

arXiv2020

Overall Agreement for Multiple Raters with Replicated Measurements

Multiple raters are often needed to be used interchangeably in practice for measurement or evaluation. Assessing agreement among these multiple raters via agreement indices are necessary before their participation. While the intuitively appealing agreement indices such as coverage probability and total deviation index, and relative area under coverage probability curve, have been extended for assessing overall agreement among multiple raters, these extensions have limitations. The existing overall agreement indices either require normality and homogeneity assumptions or did not preserve the intuitive interpretation of the indices originally defined for two raters. In this paper, we propose a new set of overall agreement indices based on maximum pairwise differences among all raters. The proposed new overall coverage probability, overall total deviation index and relative area under overall coverage probability curve retain the original intuitive interpretation from the pairwise version. Without making any distributional assumption, we also propose a new unified nonparametric estimation and inference approach for the overall indices based on generalized estimating equations that can accommodate replications made by the same rater. Under mild assumptions, the proposed variance estimator is shown to achieve efficiency bound under independent working correlation matrix. Simulation studies under different scenarios are conducted to assess the performance of the proposed estimation and inference approach with and without replications. We illustrate the methodology by using a blood pressure data with three raters who made three replications on each subjects.

stat.ME↗