twoStagePower {genepi}

Calculate the power of a two stage design for GWAS

Description

Calculate the power of a two stage design for GWAS under sample size or cost constraints. Implements methods in the refereces below.

Usage

twoStagePower(n=NULL, Cost=NULL, m=5000, mu=0.045, mu.loc=0.5, p=0.10,
            f=NULL, relcost=1, true.needed=1, rho=0, rho0=0, nsim=2000)

Arguments

n

The maximum sample size for the study.

Cost

Maximum available resource. One of Cost or n must be specified.

m

The number of markers in the study. Default is 5000. It will take a a long time to compute power for very large numbers e.g. 100000

mu

The mean vector for the markers that are associated with endpoint.

mu.loc

The locations of the true markers. Since the chromosome is mapped to the unit interval (0,1) the numbers should be between 0 and 1.

p

The proportion of markers taken to the second stage. The default is 0.1 which is found to be optimal.

f

The fraction of Cost or sample size allocated to the first stage. If not specified it uses 0.75 for the Cost constraint scenario and 0.5 for the sample size contraint scenario.

relcost

Specifies how expensive it is to genotype in the second stage compared to the first stage.

true.needed

The number of markers selected in the end. Can be a maximum of length of mu.loc (or mu).

rho, rho0

correlation between markers

nsim

Number of Monte Carlo replications to compute power.

Details

This implements the method in the reference below.

Value

It returns the power as a single numeric value

Author(s)

Jaya M. Satagopan & Venkatraman E. Seshan

References

Satagopan JM, Venkatraman ES, Begg CB. (2004) Two-stage designs for gene-disease association studies with sample size constraints. Biometrics

Examples

twoStagePower(n=1000)
twoStagePower(Cost=1000)