Dear all,
I have a dataset in which I have a binary variable as an outcome (adverse events, var "event") detected at different timepoints (var "followup") and the patients ("id") are clustered in different hospitals (var "center").
The objective of my analysis is to evaluate how some covariates affect the possibility of an adverse event.
Model
melogit event i.cov1 i.cov2 ....... || center: || id:
does not converge.
I believe that this is due to a very low within-patient variability (almost always a patient always has adverse events, or never).
I would try to assume an independence matrix of the within-patient residuals (to reduce the model to a logit model with the within-hospital clustering level only) but I can't figure out how to do it. I thought you could use a "residuals" option but that's not the case.
Anyone have an idea?
Thank you
Gianfranco
I have a dataset in which I have a binary variable as an outcome (adverse events, var "event") detected at different timepoints (var "followup") and the patients ("id") are clustered in different hospitals (var "center").
The objective of my analysis is to evaluate how some covariates affect the possibility of an adverse event.
Model
melogit event i.cov1 i.cov2 ....... || center: || id:
does not converge.
I believe that this is due to a very low within-patient variability (almost always a patient always has adverse events, or never).
I would try to assume an independence matrix of the within-patient residuals (to reduce the model to a logit model with the within-hospital clustering level only) but I can't figure out how to do it. I thought you could use a "residuals" option but that's not the case.
Anyone have an idea?
Thank you
Gianfranco
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