Kernel-based multi-marker tests for survival outcomes use primarily the Cox model to adjust for covariates. The proportional hazards assumption made by the Cox model could be unrealistic, especially in the long-term follow-up. We develop a suite of novel multi-marker survival tests for genetic association based on the accelerated failure time model, which is a popular alternative to the Cox model due to its direct physical interpretation. The tests are based on the asymptotic distributions of their test statistics and are thus computationally efficient. The association tests can account for the heterogeneity of genetic effects across sub-populations/individuals to increase the power. All the new tests can deal with competing risks and left truncation. Moreover, we develop small-sample corrections to the tests to improve their accuracy under small samples. Extensive numerical experiments show that the new tests perform very well in various scenarios. An application to a genetic dataset of Alzheimer's disease illustrates the tests' practical utility.

翻译：针对生存结局的核函数多标记关联检验主要采用Cox模型进行协变量校正。然而Cox模型的比例风险假设在长期随访等场景中可能不切实际。我们基于加速失效时间模型（作为Cox模型的常用替代方案，因其具有直观的物理解释性）开发了一套新型多标记生存遗传关联检验方法。这些检验基于检验统计量的渐近分布模型，因此计算效率高。关联检验可考虑遗传效应在亚群/个体间的异质性以提高统计效能。所有新方法均能处理竞争风险与左截断问题。此外，我们开发了小样本校正方法以提升检验在样本量较小时的准确性。大量数值实验表明，新方法在多种场景下表现优异。阿尔茨海默病遗传数据集的应用案例验证了这些方法的实际效用。