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SurvivalGWAS_Power (v1.5) [Formerly known as PhASTESt]

With the increased scale of genome-wide pharmacogenomic 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 over a range of study designs.

There are already genetic power calculators available for binary phenotypes and quantitative traits, but the key outcome of interest in pharmacogenomic studies is often “time to event” (survival) data, which cannot adequately be modelled by existing software. To address this issue, we have developed the user friendly software tool SurvivalGWAS_Power to perform power calculations and generate sample pharmacogenomic data with time to event outcomes over a range of study designs and different analytical models. SurvivalGWAS_Power calculates the power to detect association of a SNP with a time to event outcome (at a pre-specified significance threshold) over a range of study design scenarios. The data can be analysed by means of a Cox proportional hazards model or Weibull regression model to account for non-proportional data, and can account for treatment and SNP-treatment interaction effects. To allow for flexibility of analysis using methods, which are not supported by the power calculator, individual simulated data sets can also be output from the software.

Download

Download SurvivalGWAS_Power

Installation instructions

  1. Click on the download link.
  2. Once downloaded, extract files and open the setup.exe file or SurvivalGWAS_Power.msi file.
  3. Follow the software wizard instructions for installation.

Link to publication

http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1407-9

Example

Download SurvivalGWAS_Power Example (PDF)

New features & Bug fixes

Version 1.5 changes (01/12/2017):

  • Power calculation based on LRT of joint association. Compares one model with the SNP, Treatment and interaction effect vs. a the null model with only the treatment main effects. 2 degree of freedom Chi-squared test. Useful to assess bias when interaction effect is not accounted for in the analysis.

Version 1.4.1 changes (15/05/2017):

  • The input parameter for the SNP effect was taken as a negative into the simulation of survival times, despite specifying it as positive. This has been changed to a positive value.

Contact

Hamzah Syed
Block F, Waterhouse Building
Department of Biostatistics
University of Liverpool
Email: hsyed@liverpool.ac.uk

Andrew P Morris
Email: A.P.Morris@liverpool.ac.uk

Andrea L Jorgensen
Email: aljorgen@liverpool.ac.uk

FAQ

Please submit your question via email to hsyed@liverpool.ac.uk and we will respond as soon as possible.

All questions and responses will be published on this page below.