gap (version 1.6)

tscc: Power calculation for two-stage case-control design

Description

Power calculation for two-stage case-control design

Usage

tscc(model, GRR, p1, n1, n2, M, alpha.genome, pi.samples, pi.markers, K)

Value

The returned value is a list containing a copy of the input plus output as follows,

• model any in c("multiplicative","additive","dominant","recessive").

• GRR genotype relative risk.

• p1 the estimated risk allele frequency in cases.

• pprime expected risk allele frequency in cases.

• p expected risk allele frequency in controls.

• n1 total number of cases.

• n2 total number of controls.

• M total number of markers.

• alpha.genome false positive rate at genome level.

• pi.samples sample% to be genotyped at stage 1.

• pi.markers markers% to be selected (also used as the false positive rate at stage 1).

• K the population prevalence.

• C threshoulds for no stage, stage 1, stage 2, joint analysis.

• power power corresponding to C.

Arguments

model

any in c("multiplicative","additive","dominant","recessive").

GRR

genotype relative risk.

p1

the estimated risk allele frequency in cases.

n1

total number of cases.

n2

total number of controls.

M

total number of markers.

alpha.genome

false positive rate at genome level.

pi.samples

sample% to be genotyped at stage 1.

pi.markers

markers% to be selected (also used as the false positive rate at stage 1).

K

the population prevalence.

Author

Jing Hua Zhao

Details

This function gives power estimates for two-stage case-control design for genetic association.

The false positive rates are calculated as follows,

$$P(|z1|>C1)P(|z2|>C2,sign(z1)=sign(z2))$$ and $$P(|z1|>C1)P(|zj|>Cj||z1|>C1)$$ for replication-based and joint analyses, respectively; where C1, C2, and Cj are threshoulds at stages 1, 2 replication and joint analysis,

$$z1 = z(p1,p2,n1,n2,pi.samples)$$ $$z2 = z(p1,p2,n1,n2,1-pi.samples)$$ $$zj = sqrt(pi.samples)*z1+sqrt(1-pi.samples)*z2$$

References

skol06gap

Examples

if (FALSE) {
K <- 0.1
p1 <- 0.4
n1 <- 1000
n2 <- 1000 
M <- 300000
alpha.genome <- 0.05
GRR <- 1.4
p1 <- 0.4
pi.samples <- 0.2
pi.markers <- 0.1

options(echo=FALSE)
cat("sample%,marker%,GRR,(thresholds x 4)(power estimates x 4)","\n")
for(GRR in c(1.3,1.35,1.40))
{
   cat("\n")
   for(pi.samples in c(1.0,0.5,0.4,0.3,0.2))
   {
      if(pi.samples==1.0) s <- 1.0
      else s <- c(0.1,0.05,0.01)
      for(pi.markers in s)
      {
        x <- tscc("multiplicative",GRR,p1,n1,n2,M,alpha.genome,
                  pi.samples,pi.markers,K)
        l <- c(pi.samples,pi.markers,GRR,x$C,x$power)
        l <- sprintf("%.2f %.2f %.2f, %.2f %.2f %.2f %.2f, %.2f %.2f %.2f %.2f",
                     l[1],l[2],l[3],l[4],l[5],l[6],l[7],l[8],l[9],l[10],l[11])
        cat(l,"\n")
      }
      cat("\n")
   }
}
options(echo=TRUE)
}