Dynamic (or varying) covariate effects often manifest meaningful physiological mechanisms underlying chronic diseases. However, a static view of covariate effects is typically adopted by standard approaches to evaluating disease prognostic factors, which can result in depreciation of some important disease markers. To address this issue, in this work, we take the perspective of globally concerned quantile regression, and propose a flexible testing framework suited to assess either constant or dynamic covariate effects. We study the powerful Kolmogorov–Smirnov (K–S) and Cramér–Von Mises (C–V) type test statistics and develop a simple resampling procedure to tackle their complicated limit distributions. We provide rigorous theoretical results, including the limit null distributions and consistency under a general class of alternative hypotheses of the proposed tests, as well as the justifications for the presented resampling procedure. Extensive simulation studies and a real data example demonstrate the utility of the new testing procedures and their advantages over existing approaches in assessing dynamic covariate effects.

动态（或变化的）协变量效应通常表现出慢性疾病背后有意义的生理机制。然而，标准方法通常采用协变量效应的静态视图来评估疾病预后因素，这可能导致一些重要疾病标志物的贬值。为了解决这个问题，在这项工作中，我们从全球关注的分位数回归的角度，提出了一个灵活的测试框架，适合评估恒定或动态协变量效应。我们研究了强大的 Kolmogorov–Smirnov (K–S) 和 Cramér–Von Mises (C–V) 类型测试统计数据，并开发了一个简单的重采样程序来解决它们复杂的极限分布。我们提供严谨的理论成果，包括在拟议检验的一般类替代假设下的极限零分布和一致性，以及所提出的重采样程序的理由。广泛的模拟研究和真实数据示例证明了新测试程序的实用性及其在评估动态协变量效应方面优于现有方法的优势。