Re: [NMusers] Mixture model with logistic regression

From: Mark Sale <>
Date: Sat, 20 Feb 2016 20:01:50 +0000


     Thanks for your insights. But, the issue isn't the post hoc values. W=
ith the mixture model the OMEGA on the intercept is huge (680), and the ent=
ire population is in the low intercept value group (Intercept = -11). Th=
en to accommodate the patients with frequent AEs, it assigns a (post hoc) E=
TA of +15, giving an individual value for intercept of 6 (and a probability=
 of the AE of ~1, as it should. My question is whey does it refuse to simp=
ly put those 8% of the patients in a sub population with intercept = 6, E=
TA=0. rather than saying the expected value is -11, with ETA = +15. Eve=
n when I fix the fractions in the subpopulations for the observed values, a=
nd fix OMEGA to a small, reasonable value, and fix the intercept values f=
or the 3 populations to reasonable values it will still do this. The only =
thing that has worked is to assign each subject to the apparent population =
in the data set.


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

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From: Matts Kågedal <>
Sent: Saturday, February 20, 2016 2:44 PM
To: Mark Sale
Subject: Re: [NMusers] Mixture model with logistic regression

Hi Mark,
The pattern you see in the posthocs could possibly be a shrinkage phenomeno=
n. I.e. patients with AE most of the time will have the same ETA, while pat=
ients with no AE will have the same ETA and there will be a third group in =
 between. If shrinkage is causing this, you should not expect any improveme=
nt with a mixture model. Before you reject your original model I would ther=
efore also evaluate it by simulation and re-estimation. I think it is quite=
 possible that you will retreive a similar pattern in the posthocs even whe=
n you simulate based on a normal distribution.
Matts Kågedal
Pharmacometrics, Genentech.

On Fri, Feb 19, 2016 at 2:30 PM, Mark Sale <<mailto:msale=>> wrote:

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. ™
2525 Meridian Parkway, Suite 280
Research Triangle Park, NC 27713
Office (919)-973-0383<tel:%28919%29-973-0383><><>

Received on Sat Feb 20 2016 - 15:01:50 EST

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