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

Kernel-based multi-marker tests of association based on the accelerated failure time model