multilevel logistic regression over time
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multilevel logistic regression over time
 
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Listers, Is it possible to run a longitudinal multilevel logistic
regression? I’m particularly interested in doing this with dyadic data a
la Kenny’s Actor Partner Interdependence Model. And if so, are there any good guides for how to do this? And
if not are there any tutorials to run this type of analysis with other
software, specifically SAS? I’ve come across some for SAS and multilevel
logistic with dyadic data but I’m not sure how easy it will be to add the
longitudinal component to that. Thanks Matt Matthew Pirritano, Ph.D. Research Analyst IV Medical Services Initiative (MSI) Orange County Health Care Agency (714) 568-5648 |
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Matt,
I'm not familiar with the model to which you are referring, but let's see if I can help get you started. Suppose your study design consists of couples who are assigned to one of two treatment conditions who are then repeatedly measured over time (e.g., 4 time points) on a dichotomous response variable. The data set is structured vertically as followed: subj_ID couple_ID cond time y 1 1 1 1 0 2 1 1 1 1 1 1 1 2 1 2 1 1 2 1 1 1 1 3 1 2 1 1 3 0 1 1 1 4 0 2 1 1 4 0 1 2 2 1 0 2 2 2 1 1 1 2 2 2 0 2 2 2 2 1 1 2 2 3 1 2 2 2 3 1 1 2 2 4 1 2 2 2 4 0 1 3 1 1 1 2 3 1 1 1 1 3 1 2 1 2 3 1 2 0 1 3 1 3 1 2 3 1 3 0 1 3 1 4 1 2 3 1 4 1 . . . Note that subject 1 from the first couple is NOT the same person as subject 1 from the second couple. That is, the dataset is structured such that subject identification numbers are nested within couple identification numbers (This point is important when it comes to construction of the second RANDOM statement specified below).
The primary research question is whether or not there is a different response between treatment conditions over time, taking into account nesting of subjects in couples and repeated measures. With that stated, here's some SAS code: proc glimmix data=mydata method=laplace; class cond time subj_ID couple_ID; model y (event=last) = cond|time / s link=logit dist=binary; random = int / subject=couple_ID; random = int / subject=subj_ID(couple_ID); run; The GLIMMIX invocation statement specifies employment of a laplace approximation method, although the adaptive quadrature method would be preferred if computationally feasible. The CLASS statement specifies all categorical predictor variables, including the subject variables. The MODEL statements assumes a 2X4 fixed effects design matrix including the main effects, condition and time, as well as the interaction effect, condition-BY-time. The first RANDOM statement accounts for nesting of subjects within couples to account for within-couple correlations. The second RANDOM statement assumes a compound symmetric correlation among within-subject measurements collected repeatedly over time. Certainly, there are more sophisticated [and perhaps more appropriate random effects design matrices to consider], but I think this is a reasonable parameterization that has the chance of not being too computationally intensive.
View this post as a starting point upon which you can modify to more accurately fit your particular study. HTH, Ryan On Wed, Nov 30, 2011 at 3:37 PM, Pirritano, Matthew <[hidden email]> wrote: > > Listers, > > > > Is it possible to run a longitudinal multilevel logistic regression? I’m particularly interested in doing this with dyadic data a la Kenny’s Actor Partner Interdependence Model. > > > > And if so, are there any good guides for how to do this? And if not are there any tutorials to run this type of analysis with other software, specifically SAS? I’ve come across some for SAS and multilevel logistic with dyadic data but I’m not sure how easy it will be to add the longitudinal component to that. > > > > Thanks > > Matt > > > > Matthew Pirritano, Ph.D. > > Research Analyst IV > > Medical Services Initiative (MSI) > > Orange County Health Care Agency > (714) 568-5648 > > |
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Is there a way to handle repeated measurements of a
dichotomous DV in SPSS? I looked briefly though the MIXED syntax and there
doesn't appear to be a way to choose the logit link. Is there a way to work
around this?
Matt,From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of R B Sent: Wednesday, November 30, 2011 7:18 PM To: [hidden email] Subject: Re: multilevel logistic regression over time I'm not familiar with the model to which you are referring, but let's see if I can help get you started. Suppose your study design consists of couples who are assigned to one of two treatment conditions who are then repeatedly measured over time (e.g., 4 time points) on a dichotomous response variable. The data set is structured vertically as followed: subj_ID couple_ID cond time y 1 1 1 1 0 2 1 1 1 1 1 1 1 2 1 2 1 1 2 1 1 1 1 3 1 2 1 1 3 0 1 1 1 4 0 2 1 1 4 0 1 2 2 1 0 2 2 2 1 1 1 2 2 2 0 2 2 2 2 1 1 2 2 3 1 2 2 2 3 1 1 2 2 4 1 2 2 2 4 0 1 3 1 1 1 2 3 1 1 1 1 3 1 2 1 2 3 1 2 0 1 3 1 3 1 2 3 1 3 0 1 3 1 4 1 2 3 1 4 1 . . . Note that subject 1 from the first couple is NOT the same person as subject
1 from the second couple. That is, the dataset is structured such that subject
identification numbers are nested within couple identification numbers (This
point is important when it comes to construction of the second RANDOM statement
specified below).
The primary research question is whether or not there is a different
response between treatment conditions over time, taking into account nesting of
subjects in couples and repeated measures. With that stated, here's some SAS
code: proc glimmix data=mydata method=laplace; class cond time subj_ID couple_ID; model y (event=last) = cond|time / s link=logit dist=binary; random = int / subject=couple_ID; random = int / subject=subj_ID(couple_ID); run; The GLIMMIX invocation statement specifies employment of a laplace approximation method, although the adaptive quadrature method would be preferred if computationally feasible. The CLASS statement specifies all categorical predictor variables, including the subject variables. The MODEL statements assumes a 2X4 fixed effects design matrix including the main effects, condition and time, as well as the interaction effect, condition-BY-time. The first RANDOM statement accounts for nesting of subjects within couples to account for within-couple correlations. The second RANDOM statement assumes a compound symmetric correlation among within-subject measurements collected repeatedly over time. Certainly, there are more sophisticated [and perhaps more appropriate
random effects design matrices to consider], but I think this is a reasonable
parameterization that has the chance of not being too computationally
intensive.
View this post as a starting point upon which you can modify to more
accurately fit your particular study. HTH, Ryan On Wed, Nov 30, 2011 at 3:37 PM, Pirritano, Matthew <[hidden email]> wrote: > > Listers, > > > > Is it possible to run a longitudinal multilevel logistic regression? I’m particularly interested in doing this with dyadic data a la Kenny’s Actor Partner Interdependence Model. > > > > And if so, are there any good guides for how to do this? And if not are there any tutorials to run this type of analysis with other software, specifically SAS? I’ve come across some for SAS and multilevel logistic with dyadic data but I’m not sure how easy it will be to add the longitudinal component to that. > > > > Thanks > > Matt > > > > Matthew Pirritano, Ph.D. > > Research Analyst IV > > Medical Services Initiative (MSI) > > Orange County Health Care Agency > > (714) 568-5648 > > |
Re: multilevel logistic regression over time
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Carol, look at Genlinmixed. I’ve never used it but From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of Parise, Carol A. Is there a way to handle repeated measurements of a dichotomous DV in SPSS? I looked briefly though the MIXED syntax and there doesn't appear to be a way to choose the logit link. Is there a way to work around this? From: SPSSX(r) Discussion [hidden email] On Behalf Of R B Matt, Note that subject 1 from the first couple is NOT the same person as subject 1 from the second couple. That is, the dataset is structured such that subject identification numbers are nested within couple identification numbers (This point is important when it comes to construction of the second RANDOM statement specified below). The primary research question is whether or not there is a different response between treatment conditions over time, taking into account nesting of subjects in couples and repeated measures. With that stated, here's some SAS code: Certainly, there are more sophisticated [and perhaps more appropriate random effects design matrices to consider], but I think this is a reasonable parameterization that has the chance of not being too computationally intensive. View this post as a starting point upon which you can modify to more accurately fit your particular study. > > Listers, > > > > Is it possible to run a longitudinal multilevel logistic regression? I’m particularly interested in doing this with dyadic data a la Kenny’s Actor Partner Interdependence Model. > > > > And if so, are there any good guides for how to do this? And if not are there any tutorials to run this type of analysis with other software, specifically SAS? I’ve come across some for SAS and multilevel logistic with dyadic data but I’m not sure how easy it will be to add the longitudinal component to that. > > > > Thanks > > Matt > > > > Matthew Pirritano, Ph.D. > > Research Analyst IV > > Medical Services Initiative (MSI) > > Orange County Health Care Agency > > (714) 568-5648 > > |
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In reply to this post by parisec
The GENLIN procedure is certainly capable of fitting a GEE model (e.g., repeated measures logistic regression). Now, if you have both hierarchical data (e.g., nesting of students in schools) and repeated measures, then you should consider a generalized linear mixed model via the GENLINMIXED procedure.
Ryan
On Thu, Dec 1, 2011 at 12:10 PM, Parise, Carol A. <[hidden email]> wrote:
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