genSimDataGLMEM: Generate simulated data from logistic mixed effects model...
In riskPredictClustData: Assessing Risk Predictions for Clustered Data

Description Usage Arguments Details Value Author(s) References Examples

Generate simulated data from logistic mixed effects model based on the AMD data.

genSimDataGLMEM(
  nSubj = 131, 
  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)

`nSubj`	integer. Number of subjects. Each subject would have data for 2 eyes.
`beta0`	mean of intercept beta_{0i}, which is assumed random and follows normal distribution N(beta_0, sigma^2_{beta}).
`sd.beta0i`	standard deviation sigma^2_{beta} of the random intercept beta_{0i}.
`beta1`	slope for the binary covariate cursmk (current smoking status). cursmk=1 indicates current smokers. cursmk=0 indicates past smokers or never smokers.
`beta2`	slope for the continuous mean-centered covariate lncalor_c.
`beta3`	slope for the binary covariate inieye3 indicating if an eye of a subject has initial grade equal to 3. inieye3=1 indicates the eye has initial grade equal to 3.
`beta4`	slope for the binary covariate inieye4 indicating if an eye of a subject has initial grade equal to 4. inieye4=1 indicates the eye has initial grade equal to 4.
`beta5`	slope for the binary covariate rtotfat_1 indicating if the subject's total fat intake is in the 2nd quartile of total fat intake. rtotfat_1=1 indicates the subject is in the 2nd quartile.
`beta6`	slope for the binary covariate rtotfat_2 indicating if the subject's total fat intake is in the 3rd quartile of total fat intake. rtotfat_2=1 indicates the subject is in the 3rd quartile.
`beta7`	slope for the binary covariate rtotfat_3 indicating if the subject's total fat intake is in the 4th quartile of total fat intake. rtotfat_3=1 indicates the subject is in the 4th quartile.
`p.smkcur`	proportion of current smokers.
`p.inieye31`	proportion of left eye having inital grade equal to 3.
`p.inieye32`	proportion of right eye having inital grade equal to 3.
`p.inieye41`	proportion of left eye having inital grade equal to 4.
`p.inieye42`	proportion of right eye having inital grade equal to 4.
`sd.lncalorc`	standard deviation for lncalor_c.

We generate simulated data set from the following generalized linear mixed effects model:

logit(p_{ij})=beta_{0i}+beta_1 smkcur_i+ beta_2 lncalor_{ci} + beta_3 inieye3_{ij} + beta_4 inieye4_{ij} +β_5 rtotfat_{1i} +β_6 rtotfat_{2i} + β_7 rtotfat_{3i},

i=1, ..., N, j=1, 2, beta_{0i}~ N(beta_0, sigma^2_{β}).

A data frame with 8 columns: cid, subuid, prog, smkcur, lncalorc, inieye3, inieye4, and rtotfat, where cid is the subject id, subuid is the unit id, and prog is the progression status. prog=1 indicates the eye is progressed. prog=0 indicates the eye is not progressed. There are nSubj*2 rows. The first nSubj rows are for the left eyes and the second nSubj rows are for the right eyes.

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.

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,])