asypow: Calculating the asymptotic power and variance

In survSNP: Power Calculations for SNP Studies with Censored Outcomes

Calculating the asymptotic power and variance

Description

This function calculates the asymptotic power and variance assuming that the survival distribution is a mixture of exponentials with rates and the censoring distribution is uniform on the interval (a,b).

Usage

asypow(n, theta, a, b, lambda0, q, p, alpha, z,exactvar)

Arguments

n	Sample size
theta	Effect size (log genotype hazard ratio (GHR))
a	Censoring distribution parameter (assumed to be uniform on [a,b])
b	Censoring distribution parameter (assumed to be uniform on [a,b])
lambda0	Baseline exponential hazard rate
q	Relative risk allele frequency
p	Relative genotype frequency
alpha	Nominal two-sided type I error rate
z	Genotype scores (right now only additive scores AA=0,AB=1,BB=2 generate correct power)
exactvar	Indicator for using the exact variance formula

Details

This function is called by sim.snp.expsurv.power to calculate the asymptotic variance (exact and approximate) formulas. It is not intended to be called directly by the user. To conduct power calculations, use sim.snp.expsurv.power or the convenience wrapper function survSNP.power.table.

Value

power	Asymptotic power based on exact variance formula
power0	Asymptotic power based on approximate variance formula
v1	First term of asymptotic variance
v2	Second term of asymptotic variance
v12	Third term of the asymptotic variance (covariance)
vapprox	Approximate asymptotic variance formula (=v1)
exact	Exact asymptotic variance formula (=v1+v2+v12)
diff	Difference between variances (=v2+v12)
ratio	Ratio of variances (=v1/(v1+v2+v12))

Author(s)

Kouros Owzar, Zhiguo Li, Nancy Cox, Sin-Ho Jung and Chanhee Yi

References

Kouros Owzar, Zhiguo Li, Nancy Cox and Sin-Ho Jung. Power and Sample Size Calculations for SNP Association Studies with Censored Time-to-Event Outcomes. https://onlinelibrary.wiley.com/doi/full/10.1002/gepi.21645

survSNP documentation built on Feb. 16, 2023, 10:10 p.m.