powerCal {riskPredictClustData} | R Documentation |
Calculate the power for testing \delta=0
Description
Calculate the power for testing \delta=0
.
Usage
powerCal(
nSubj,
mu1,
triangle,
rho,
rho11,
rho22,
rho12,
p11,
p10,
p01,
alpha = 0.05)
Arguments
nSubj |
integer. number of subjects to be generated. Assume each subject has two observations.
|
mu1 |
\mu_1=H(Y)-H(Y_c) is the difference between probit transformation
H(Y) and probit-shift alternative H(Y_c) ,
where Y is the prediction score of a randomly selected progressing
subunit, and Y_c is the counterfactual random variable
obtained if each subunit that had progressed actually had not progressed.
|
triangle |
the difference of the expected value the the extended Mann-Whitney U statistics
between two prediction rules, i.e., \triangle = \eta^{(1)}_c - \eta^{(2)}_c
|
rho |
\rho=corr\left(H\left(Z_{ij}\right), H\left(Z_{k\ell}\right)\right) , where H=\Phi^{-1} is the probit transformation.
|
rho11 |
\rho_{11}=corr\left(H_{ij}^{(1)}, H_{i\ell}^{(1)}\right) , where H=\Phi^{-1} is the probit transformation.
|
rho22 |
\rho_{22}=corr\left(H_{ij}^{(2)}, H_{i\ell}^{(2)}\right) , where H=\Phi^{-1} is the probit transformation.
|
rho12 |
\rho_{12}=corr\left(H_{ij}^{(1)}, H_{i\ell}^{(2)}\right) , where H=\Phi^{-1} is the probit transformation.
|
p11 |
p_{11}=Pr(\delta_{i1}=1 \& \delta_{i2}=1) , where \delta_{ij}=1 if the j -th subunit of the
i -th cluster has progressed.
|
p10 |
p_{10}=Pr(\delta_{i1}=1 \& \delta_{i2}=0) , where \delta_{ij}=1 if the j -th subunit of the
i -th cluster has progressed.
|
p01 |
p_{01}=Pr(\delta_{i1}=0 \& \delta_{i2}=1) , where \delta_{ij}=1 if the j -th subunit of the
i -th cluster has progressed.
|
alpha |
type I error rate
|
Value
the power
Author(s)
Bernard Rosner <stbar@channing.harvard.edu>,
Weiliang Qiu <Weiliang.Qiu@gmail.com>,
Meiling Ting Lee <MLTLEE@umd.edu>
References
Rosner B, Qiu W, and Lee MLT.
Assessing Discrimination of Risk Prediction Rules in a Clustered Data Setting.
Lifetime Data Anal. 2013 Apr; 19(2): 242-256.
Examples
set.seed(1234567)
mu1 = 0.8
power = powerCal(nSubj = 30, mu1 = mu1,
triangle = 0.05, rho = 0.93, rho11 = 0.59, rho22 = 0.56, rho12 = 0.52,
p11 = 0.115, p10 = 0.142, p01 = 0.130, alpha = 0.05)
print(power)
[Package
riskPredictClustData version 0.2.6
Index]