For many clinical studies in cancer, germline DNA is prospectively collected for the purpose of discovering or validating single-nucleotide polymorphisms (SNPs) associated with clinical outcomes. The primary clinical endpoint for many of these studies are time-to-event outcomes such as time of death or disease progression which are subject to censoring mechanisms. The Cox score test can be readily employed to test the association between a SNP and the outcome of interest. In addition to the effect and sample size, and censoring distribution, the power of the test will depend on the underlying genetic risk model and the distribution of the risk allele. We propose a rigorous account for power and sample size calculations under a variety of genetic risk models without resorting to the commonly used contiguous alternative assumption. Practical advice along with an open-source software package to design SNP association studies with survival outcomes are provided.