Power for population-based association design
This function implements Long et al. (1997) statistics for population-based association design. This is based on a contingency table test and accurate level of significance can be obtained by Fisher's exact test.
pbsize(kp, gamma=4.5, p=0.15, alpha=5e-8, beta=0.2)
kp |
population disease prevalence |
gamma |
genotype relative risk assuming multiplicative model |
p |
frequency of disease allele |
alpha |
type I error rate |
beta |
type II error rate |
The returned value is scaler containing the required sample size
Scott WK, Pericak-Vance MA, et al. (1997). Genetic analysis of complex diseases. Science 275: 1327.
Long AD, Grote MN, Langley CH (1997). Genetic analysis of complex traits. Science 275: 1328.
Rosner B (2000). Fundamentals of Biostatistics, 5th Edition, Duxbury.
Armitage P, Colton T (2005). Encyclopedia of Biostatistics, 2nd Edition, Wiley.
extracted from rm.c
Jing Hua Zhao
kp <- c(0.01,0.05,0.10,0.2)
models <- matrix(c(
4.0, 0.01,
4.0, 0.10,
4.0, 0.50,
4.0, 0.80,
2.0, 0.01,
2.0, 0.10,
2.0, 0.50,
2.0, 0.80,
1.5, 0.01,
1.5, 0.10,
1.5, 0.50,
1.5, 0.80), ncol=2, byrow=TRUE)
outfile <- "pbsize.txt"
cat("gamma","p","p1","p5","p10","p20\n",sep="\t",file=outfile)
for(i in 1:dim(models)[1])
{
g <- models[i,1]
p <- models[i,2]
n <- vector()
for(k in kp) n <- c(n,ceiling(pbsize(k,g,p)))
cat(models[i,1:2],n,sep="\t",file=outfile,append=TRUE)
cat("\n",file=outfile,append=TRUE)
}
table5 <- read.table(outfile,header=TRUE,sep="\t")
unlink(outfile)
# Alzheimer's disease
g <- 4.5
p <- 0.15
alpha <- 5e-8
beta <- 0.2
z1alpha <- qnorm(1-alpha/2) # 5.45
z1beta <- qnorm(1-beta)
q <- 1-p
pi <- 0.065 # 0.07 and zbeta generate 163
k <- pi*(g*p+q)^2
s <- (1-pi*g^2)*p^2+(1-pi*g)*2*p*q+(1-pi)*q^2
# LGL formula
lambda <- pi*(g^2*p+q-(g*p+q)^2)/(1-pi*(g*p+q)^2)
# mine
lambda <- pi*p*q*(g-1)^2/(1-k)
n <- (z1alpha+z1beta)^2/lambda
cat("\nPopulation-based result: Kp =",k, "Kq =",s, "n =",ceiling(n),"\n")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.