pbsize2 | R Documentation |
Power for case-control association design
pbsize2(
N,
fc = 0.5,
alpha = 0.05,
gamma = 4.5,
p = 0.15,
kp = 0.1,
model = "additive"
)
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". |
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.
The returned value is the power for the specified design.
The design follows that of pbsize
.
## Not run:
# 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))
## End(Not run)
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