[R] logistic regression for repeated measurement

Frank E Harrell Jr fharrell at virginia.edu
Wed Mar 5 03:33:57 CET 2003

On Wed, 5 Mar 2003 11:14:37 +0900
Hiroto Miyoshi <h_m_ at po.harenet.ne.jp> wrote:

> Dear R-users
> I need your help.
> I have a data set which was collected from 
> an experiment of one between- and one 
> within-subject design. And the response 
> data is coded by success(1)/failure(0).
> The experiment had two groups of subjects:
> The one was experimental, and the 
> other, control.  The experimental group
> got a task training, and both groups of subjects
> were tested twice, once before the training
> and once after the training. at the same time.
> I like to examine the effect of training by
> detecting an interaction effect of the group and 
> tests. 
> Now, it seems glm is not appropriate to this 
> situation since it does not deal with stratified
> errors.
> Could you lead me to appropriate functions?
> Sincerely
> -----------------------
> Hiroto Miyoshi
> h_m_ at po.harenet.ne.jp

There are several ways to go.  GEE is one, random effects models another.  One other approach is to install the Hmisc and Design packages (http://hesweb1.med.virginia.edu/biostat/s) and do (assume id is the unique subject identifier):

f <- lrm(y ~ x1 + x2*x3 + ..., x=T, y=T) # working independence model
g <- robcov(f, id)         # cluster sandwich variance adjustment
h <- bootcov(f, id, B=100) # cluster bootstrap adjustment
summary(g)  # etc.
Frank E Harrell Jr              Prof. of Biostatistics & Statistics
Div. of Biostatistics & Epidem. Dept. of Health Evaluation Sciences
U. Virginia School of Medicine  http://hesweb1.med.virginia.edu/biostat

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