Power for case-control association design

Power for case-control association design

Usage

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

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".

Value

The returned value is the power for the specified design.

Details

This extends pbsize from a multiplicative model for a case-control design under a range of disease models. Essentially, for given sample sizes(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.

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

See also

The design follows that of pbsize.

Examples

if (FALSE) { # \dontrun{
# single calculation
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]))

# a range of sample sizes
pbsize2(p=0.1, N=c(25,50,100,200,500), gamma=1.2, kp=.1, alpha=5e-2, model='r')
  
# a power table
m <- sapply(seq(0.1,0.9, by=0.1),
            function(x) pbsize2(p=x, N=seq(100,1000,by=100),
                        gamma=1.2, kp=.1, alpha=5e-2, model='recessive'))
colnames(m) <- seq(0.1,0.9, by=0.1)
rownames(m) <- seq(100,1000,by=100)
print(round(m,2))
} # }