SurvivalGWAS_Power

SurvivalGWAS_Power calculates statistical power for detecting associations between single nucleotide polymorphisms (SNPs) and time-to-event (survival) outcomes to support the design of pharmacogenetic studies.

Key Features:

• Time-to-Event Outcome Analysis: Handles time-to-event (survival) data for assessment of SNP associations with clinical outcomes over time.
• Versatile Study Design Scenarios: Computes power across varying sample sizes, effect sizes, and other study parameters that influence power.
• Analytical Flexibility: Supports analysis under the Cox proportional hazards model and Weibull regression model.
• Treatment and Interaction Effects: Accounts for treatment effects and SNP × treatment interactions in power calculations.
• Simulation Capabilities: Generates simulated datasets to evaluate power under complex or custom study scenarios.

Scientific Applications:

• Pharmacogenetic study design: Enables design of studies to detect genetic determinants of drug response measured as time-to-event outcomes.
• Power and sample size estimation: Estimates required sample sizes and detectable effect sizes for time-to-event genetic association studies.
• Personalized medicine research: Informs identification of genetic influences on clinical outcomes over time to support therapeutic strategy development.

Methodology:

Calculates statistical power to detect associations between SNPs and time-to-event outcomes under various study conditions, incorporates Cox proportional hazards and Weibull regression models, and can generate simulated datasets.