# riskPredictDiff: Difference of two risk prediction rules for clustered data In riskPredictClustData: Assessing Risk Predictions for Clustered Data

## Description

Difference of two risk prediction rules for clustered data.

## Usage

 1 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{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 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_{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.

## 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

  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) 

riskPredictClustData documentation built on May 1, 2019, 6:34 p.m.