gap (version 1.5-1)

pbsize2: Power for case-control association design

Description

This is a revised version of pbsize which is appropriate for a case-control design under a range of disease models. Essentially, for given sample size(s), a proportion of which (fc) being cases, the function calculates power estimate for a given type I error (alpha), genotype relative risk (gamma), frequency of the risk allele (p), the prevalence of disease in the population (kp) and optionally a disease model (model). A major difference would be the consideration of case/control ascertainment in pbsize.

Usage

pbsize2(
  N,
  fc = 0.5,
  alpha = 0.05,
  gamma = 4.5,
  p = 0.15,
  kp = 0.1,
  model = "additive"
)

Value

The returned value is the power for the specified design.

Arguments

N

The sample size.

fc

The proportion of cases in the sample.

alpha

Type I error rate.

gamma

The genotype relative risk (GRR).

p

Frequency of the risk allele.

kp

The prevalence of disease in the population.

model

Disease model, i.e., "multiplicative","additive","dominant","recessive","overdominant".

Author

Jing Hua Zhao

Details

Internally, the function obtains a baseline risk to make the disease model consistent with Kp as in tscc and should produce accurate power estimate. Note it provides power estimates for given sample size(s) only.

See Also

The design follows that of pbsize.

Examples

if (FALSE) {
# single calc
m <- c("multiplicative","recessive","dominant","additive","overdominant")
for(i in 1:5) print(pbsize2(N=50,alpha=5e-2,gamma=1.1,p=0.1,kp=0.1, model=m[i]))

# for a range of sample sizes
pbsize2(p=0.1, N=c(25,50,100,200,500), gamma=1.1, kp=.1, alpha=5e-2, model='r')
  
# create a power table
f <- function(p)     
  pbsize2(p=p, N=seq(100,1000,by=100), gamma=1.1, kp=.1, alpha=5e-2, model='recessive')
m <- sapply( X=seq(0.1,0.9, by=0.1), f)
colnames(m) <- seq(0.1,0.9, by=0.1)
rownames(m) <- seq(100,1000,by=100)
print(round(m,2))

library(genetics)
m <- c("multiplicative","recessive","dominant","partialrecessive")
for(i in 1:4) print(power.casectrl(p=0.1, N=50, gamma=1.1, kp=.1, alpha=5e-2,
    minh=m[i]))
power.casectrl(p=0.1, N=c(25,50,100,200,500), gamma=1.1, kp=.1, alpha=5e-2,
    minh='r')
f <- function(p)
  power.casectrl(p=p, N=seq(100,1000,by=100), gamma=1.1, kp=.1, alpha=5e-2,
    minh='recessive')
m <- sapply( X=seq(0.1,0.9, by=0.1), f)
colnames(m) <- seq(0.1,0.9, by=0.1)
rownames(m) <- seq(100,1000,by=100)
print(round(m,2))
}