riskPredictDiff {riskPredictClustData}

Difference of two risk prediction rules for clustered data

Description

Difference of two risk prediction rules for clustered data.

Usage

riskPredictDiff(frame, alpha = 0.05)

Arguments

frame

A data frame with 5 columns: cid, subuid, status, score1, and score2. cid indicates cluster id; subuid indicates unit ID within a cluster; status=1 indicates an eye is progressed; status=0 indicates an eye is not progressed; score1 represents the score based on prediction rule 1. score2 represents the score based on prediction rule 2.

alpha

numeric. The confidence level.

Value

A list of 7 elements:

diff

the difference of test statistics \hat{\eta}_c^{(1)}-\hat{\eta}_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 z=diff/se.diff

pval

p-value of the test

res1

output object of the function riskPredict for prediction rule 1.

res2

output object of the function riskPredict for prediction rule 2.

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_{12}=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.

Author(s)

Bernard Rosner <[e-mail]>, Weiliang Qiu <[e-mail]>, Meiling Ting Lee <[e-mail]>

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)
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)