repSampleSizeRR {RRate} | R Documentation |
Sample size determination for the replication study based on RR
Description
repSampleSizeRR
and repSampleSizeRR2
implement the RR-based sample size determination method for the replication study. If the replication study has the same control-to-case ratio with the primary study, then repSampleSizeRR
can be used. Otherwise, repSampleSize2
is more suitable.
Usage
repSampleSizeRR(GRR, n, MUhat, SE, zalpha2, zalphaR2, idx = TRUE)
repSampleSizeRR2(GRR,CCR2, MUhat,SE,fU,fA,zalpha2,zalphaR2, idx=TRUE)
Arguments
GRR |
The desired global replication rate. |
n |
Sample size in the primary study. |
MUhat |
The observed effect size (log-odds ratio). |
SE |
The standard error of MUhat. |
zalpha2 |
The critical value of z-values in the primary study, i.e. z_alpha/2. |
zalphaR2 |
The critical value of z-values in the replication study, i.e. z_alphaR/2. |
idx |
The indexes of the SNPs having been further inverstigated in the replication study. We only calculate RR for primary associations with indexes in |
CCR2 |
The control-to-case ratio of the replication study. |
fU |
The allele frequency in the control group. |
fA |
The allele frequency in the case group. |
Value
The determined sample size of the replication study is returned.
Author(s)
Wei Jiang, Jing-Hao Xue and Weichuan Yu
Maintainer: Wei Jiang <wjiangaa@connect.ust.hk>
References
Jiang, W., Xue, J-H, and Yu, W. What is the probability of replicating a statistically significant association in genome-wide association studies?. Submitted.
See Also
RRate
repRateEst
,
SEest
,
HLtest
Examples
alpha<-5e-6 #Significance level in the primary study
alphaR<-5e-3 #Significance level in the replication study
zalpha2<-qnorm(1-alpha/2)
zalphaR2<-qnorm(1-alphaR/2)
##Load data
data('smryStats1') #Example of summary statistics in 1st study
#### Sample size determination ###
n1<-4000 #Sample size of the primary study
n2_1<-repSampleSizeRR(0.8, n1, log(smryStats1$OR),smryStats1$SE,zalpha2,zalphaR2)
CCR2<-2 #Control-to-case ration in the replication study
n2_2<-repSampleSizeRR2(0.8, CCR2, log(smryStats1$OR),smryStats1$SE,smryStats1$F_U,
smryStats1$F_A,zalpha2,zalphaR2)