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RE: Mixture model with logistic regression

From: Gopalakrishnan, Mathangi <mgopalakrishnan>
Date: Sat, 20 Feb 2016 03:47:17 +0000

Use of mixture model may not be suitable here, if the underlying distributi=
on of eta's for the different subgroups is not normally distributed. Based =
on the description, it looks like you have 3 degenerate eta distributions(E=
TA ~0 for no AE; ETA ~1 for 25% AE and ETA ~10 for 7% with all AE), which v=
iolates the normality assumption.

Upon quick search, I came across this article, where they have described al=
most a similar situation as yours. They have used a two part mixture distr=
ibution to take care of the large proportions of the subjects with no AE. =
Hope it is helpful.
          Kowalski, Kenneth G., Lynn McFadyen, Matthew M. Hutmacher, Bill F=
rame, and Raymond Miller. "A Two-Part Mixture Model for Longitudinal Advers=
e Event Severity Data." Journal of Pharmacokinetics and Pharmacodynamics 30=
, no. 5 (October 2003): 315-36.


Mathangi Gopalakrishnan, MS, PhD
Research Assistant Professor
Center for Translational Medicine (CTM)
School of Pharmacy, UMB
Ph: 410-706-7842<>

From: owner-nmusers
 Behalf Of Mark Sale
Sent: Friday, February 19, 2016 5:30 PM
To: nmusers
Subject: [NMusers] Mixture model with logistic regression

Has anyone every tried to use a mixture model with logistic regression? I h=
ave data on a AE in several hundred patients, measured multiple times (10-2=
0 times per patient). Examining the data it is clear that, independent of =
drug concentration, there is very wide distribution of this AE, 68% of the =
patients never have the AE, 25% have it about 20% of the time and the rest =
have it pretty much continuously, regardless of drug concentration. (in or=
dinary logistic regression, just glm in R, there is also a nice concentrati=
on effect on the AE in addition). Running the usual logistic model, not s=
urprisingly, I get a really big ETA on the intercept, with 68% of the peopl=
e having ETA small negative, 25% ETA ~ 1 and 7% ETA ~ 10. No covariates see=
m particularly predictive of the post hoc ETA. I thought I could use a mix=
ture model, with 3 modes, but it refused to do that, giving me essentially =
0% in the 2nd and 3rd distribution, still with the really large OMEGA for t=
he intercept. Even when I FIX the OMEGA to a reasonable number, I still ge=
t essentially no one in the 2nd and 3rd distribution. I tried fixing the f=
raction in the 2nd and 3rd distribution (and OMEGA), and it still gave me a=
 very small difference in the intercept for the 2nd and 3rd populations.

Is there an issue with using mixture models with logistic regression? I'm j=
ust using FOCE, Laplacian, without interaction, and LIKE.

Any ideas?


Mark Sale M.D.
Vice President, Modeling and Simulation
Nuventra, Inc. (tm)
2525 Meridian Parkway, Suite 280
Research Triangle Park, NC 27713
Office (919)-973-0383

Received on Fri Feb 19 2016 - 22:47:17 EST

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