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

Ben Pelzer b.pelzer at maw.ru.nl
Wed Feb 28 11:09:12 CET 2018

To be correct: Snijders and Bosker and Willett and Singer explain this 
multilevel growth model for linear models only, but my hunch is that it 
can be used for logistic models as well.

On 28/02/2018 11:03, Ben Pelzer wrote:
> Hi Dennis,
> Another way to go would be to include a random intercept and a random 
> time effect (both over persons) in the logit, much like is done in 
> linear models. This creates correlation between logit values across 
> successive time-points. This is e.g. explained in Snijders and 
> Bosker's book  and in Singer and Willett. You can make the model 
> increasingly more flexible (in terms of the correlation structure over 
> time) by not only including a linear random time effect but also a 
> quadratic, cubic etc. time-effect. This is a different approach than 
> letting the error terms "e" correlate over time. But it serves the 
> same end: correlation over time.
> I think there's nothing wrong with this "multilevel growth model" 
> approach for a glm, but anyone please correct me if  I'm wrong. 
> Anyway, it can be carried with most multilevel or random effects 
> software packages, like glmer in R.
> Best regards, Ben.
> On 27/02/2018 07:22, Dennis Ruenger wrote:
>> 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
>> necessary.
>> 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.
>> Dennis
>>     [[alternative HTML version deleted]]
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