Hi Statalisters!
In linear mixed models, small sample bias is typically addressed through restricted maximum likelihood estimation (REML) and a Kenward-Roger correction. How do we go about this for the mixed effects logistic regression model to obtain results that are close to an exact methods approach? Are there other options to running this kind of analysis using exact methods?
Sample Stata code:
xi: melogit VL_afterenrol1 i.age_category i.Sex_c i.core_regimen_current i.Adherence i.months_ART_catnew i.VLatenrollement_categ, || pid:, covariance(unstructured) vce(cluster Site_coded) or intpoints(2)
I am using Stata version 15.1
Thanks for your help!
In linear mixed models, small sample bias is typically addressed through restricted maximum likelihood estimation (REML) and a Kenward-Roger correction. How do we go about this for the mixed effects logistic regression model to obtain results that are close to an exact methods approach? Are there other options to running this kind of analysis using exact methods?
Sample Stata code:
xi: melogit VL_afterenrol1 i.age_category i.Sex_c i.core_regimen_current i.Adherence i.months_ART_catnew i.VLatenrollement_categ, || pid:, covariance(unstructured) vce(cluster Site_coded) or intpoints(2)
I am using Stata version 15.1
Thanks for your help!
