Speaker
Description
Background
Triple-negative breast cancer (TNBC) represents one of the most aggressive and treatment-resistant breast cancer subtypes. Patients with locally advanced unresectable or metastatic TNBC (mTNBC) typically face a median overall survival of only 8 to 13 months, highlighting the urgent need for efficient drug evaluation strategies. Conventional statistical methods often assume normality or require complex variance estimation, which limits their applicability to heterogeneous and non-normal patient data. To accelerate drug development and improve therapeutic decision-making, robust nonparametric methods are essential.
Methods
We propose a novel nonparametric test based on ranked-set empirical distribution functions and the concept of power divergence between two empirical distributions. This distribution-free approach eliminates the reliance on normality assumptions and avoids estimation of dispersion matrices, which are prone to instability in complex or small samples. Incorporating the permutation principle further enhances reliability. Monte Carlo simulations were conducted to assess the empirical power of the proposed test under various distributional settings, including heavy-tailed, light-tailed, and elliptically asymmetric populations.
Results
Simulation results demonstrate that the proposed test achieves superior statistical power compared with conventional alternatives. It remains robust across heavy-tailed and light-tailed distributions and retains performance under elliptically asymmetric population structures. Unlike Hotelling’s T² and Chatterjee and Sen’s bivariate Wilcoxon test, our method does not require matrix inversion, thereby avoiding computational and implementation challenges. Furthermore, it extends beyond the univariate limitations of the Kolmogorov–Smirnov test by offering a true two-sample multivariate framework. Application to real-world TNBC trial data illustrates the method’s practical utility, enabling reliable efficacy comparisons while reducing susceptibility to distributional misspecification.
Conclusion
The proposed nonparametric framework enhances efficiency in cancer drug testing by providing a powerful, assumption-free alternative to conventional hypothesis testing. Its robustness to non-normal, heavy-tailed, and asymmetric data structures makes it particularly well-suited for oncology trials, where heterogeneous populations are common. By improving power, reducing reliance on restrictive assumptions, and enabling broader applicability, this approach has the potential to accelerate drug evaluation, optimize resource use, and improve therapeutic decision-making. Beyond TNBC, its versatility extends to a wide range of biomedical research contexts, supporting more efficient and reliable assessment of treatment efficacy.
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