gap (version 1.1-16)

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.

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.

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.

See Also

The design follows that of pbsize.

Examples

## Not run: 
# 
# # 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))
# 
# ## End(Not run)