Abstract
BackgroundPower calculators are currently available for the design of genetic association studies of binary phenotypes and quantitative traits, but not for “time to event” outcomes, which are of particular relevance in pharmacogenetics. With the rapid emergence of pharmacogenetic association studies of single nucleotide polymorphisms (SNPs), and the complexity of clinical outcomes they consider, there is a need for software to perform power calculations of time to event data over a range of design scenarios and analytical methodologies.ResultsWe have developed the user friendly software tool SurvivalGWAS_Power to perform power calculations for time to event outcomes over a range of study designs and different analytical approaches. The software calculates the power to detect SNP association with a time to event outcome over a range of study design scenarios. The software enables analyses under a Cox proportional hazards model or Weibull regression model, and can account for treatment and SNP-treatment interaction effects. Simulated data sets can also be generated by SurvivalGWAS_Power to enable analyses with methods that are not currently supported by the power calculator, thereby increasing the flexibility of the software.ConclusionsSurvivalGWAS_Power addresses the need for flexible and user-friendly software for power calculations for genetic association studies of time to event outcomes, with particular design features of relevance in pharmacogenetics.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1407-9) contains supplementary material, which is available to authorized users.
Highlights
Power calculators are currently available for the design of genetic association studies of binary phenotypes and quantitative traits, but not for “time to event” outcomes, which are of particular relevance in pharmacogenetics
Power calculations are an essential component of study design, and are readily available for genome-wide association studies (GWAS) of binary phenotypes and quantitative traits [1]
The simulated observed time to event outcome is generated for the following possible study design scenarios, each of which include the option of incorporating treatment and single nucleotide polymorphisms (SNPs)-treatment interaction effects
Summary
SurvivalGWAS_Power can simulate a large number of datasets to enable efficient estimation of power based on specified model parameters and design scenarios. The left histogram shows the distribution of estimated SNP effect sizes across simulations, which in this example are centred around 0.5, and not the true effect size of 0.4. This bias occurs as the data are simulated with treatment and SNP-treatment interaction effects, but the analysis model does not take these into account. The left histogram shows the distribution of estimated SNP effect sizes across simulations, which in this example are centred around 0.4. For a sample size of 1000 individuals and a scenario with censoring but no recruitment period
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
