Description Usage Arguments Value Author(s) References Examples
Difference of two risk prediction rules for clustered data.
1 | riskPredictDiff(frame, alpha = 0.05)
|
frame |
A data frame with 5 columns: cid, subuid, status, score1, and score2.
|
alpha |
numeric. The confidence level. |
A list of 7 elements:
diff |
the difference of test statistics \hat{η}_c^{(1)}-\hat{η}_c^{(2)} hateta_c^(1)-hateta_c^(2) based on the 2 prediction rules. |
se.diff |
standard error of the difference under the null hypothesis. |
z |
z score |
pval |
p-value of the test |
res1 |
output object of the function |
res2 |
output object of the function |
rhoVec |
A vector of 4 correlations: rho=cov(H_{ij}^{(1)}, H_{ij}^{(2)}) , rho_{11}=cov(H_{ij}^{(1)}, H_{it}^{(1)}) , rho_{22}=cov(H_{ij}^{(2)}, H_{it}^{(2)}) , and rho_{22}=cov(H_{ij}^{(1)}, H_{it}^{(2)}) |
E.diff.Ha |
expectation of the difference under the alternative hypothesis. |
se.diff.Ha |
standard error of the difference under the alternative hypothesis. |
CIlow.diff |
Lower confidence limit. |
CIup.diff |
Upper confidence limit. |
Bernard Rosner <stbar@channing.harvard.edu>, Weiliang Qiu <Weiliang.Qiu@gmail.com>, Meiling Ting Lee <MLTLEE@umd.edu>
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | set.seed(1234567)
datFrame = genSimDataGLMEM(nSubj = 30, beta0 = -6, sd.beta0i = 1.58,
beta1 = 1.58, beta2 = -3.95, beta3 = 3.15, beta4 = 2.06,
beta5 = 0.51, beta6 = 1.47, beta7 = 3.11,
p.smkcur = 0.08, p.inieye31 = 0.44, p.inieye32 = 0.42,
p.inieye41 = 0.12, p.inieye42 = 0.11, sd.lncalorc = 0.33)
print(dim(datFrame))
print(datFrame[1:2,])
# prediction rule 1
tt1 = getScore(fmla = prog~smkcur+lncalorc+inieye3+inieye4+factor(rtotfat),
cidVar = "cid", subuidVar = "subuid", statusVar = "prog",
datFrame = datFrame, mycorstr = "exchangeable",
verbose = FALSE)
myframe1=tt1$frame
print(dim(myframe1))
print(myframe1[1:3,])
####
# prediction rule 2
tt2 = getScore(fmla = prog~smkcur+lncalorc+inieye3+inieye4,
cidVar = "cid", subuidVar = "subuid", statusVar = "prog",
datFrame = datFrame, mycorstr = "exchangeable",
verbose = FALSE)
myframe2=tt2$frame
print(dim(myframe2))
print(myframe2[1:3,])
# combine scores from two prediction rules
myframe12=myframe1[, c("cid", "subuid", "status")]
myframe12$score1=myframe1$score
myframe12$score2=myframe2$score
print(dim(myframe12))
print(myframe12[1:3,])
####
resDiff = riskPredictDiff(frame=myframe12)
print(names(resDiff))
print(resDiff)
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