[R-sig-ME] Longitudinal logistic regression with continuous-time first-order autocorrelation structure

Dennis Ruenger dennis.ruenger at gmail.com
Tue Feb 27 07:22:17 CET 2018

Dear All.

I need to analyze an intensive longitudinal data set with a binary outcome
variable. In the “Ecological Momentary Assessment” (EMA) study,
participants received five random prompts per day for six weeks, asking
them (among other things) whether they were craving a particular drug
(yes/no). At the most basic level, I want to know whether the likelihood of
craving the drug changed across time.

Given the variable time intervals of measurement and many missing data
points, a continuous-time first-order autocorrelation model seems

I found tutorials on how to allow for continuous-time autocorrelation and
missing data in an LMM, using nlme::lme and corCAR1, but I am at a loss as
to what to do in a GLMM.

I would be thankful for any suggestions on how to analyze this kind of data
in R.


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