[NMusers] Re: Mixture model with logistic regression

From: Mark Sale <msale_at_nuventra.com>
Date: Sat, 20 Feb 2016 14:07:06 +0000


That certainly makes sense, but that options seems to not be available in N=
ONMEM, using LIKE seems to require using FOCE LAPLACE

      This is designed mainly, but not exclusively, for use with non-
      continuous observed responses ("odd-type data"). Indicates that
      Y (with NM-TRAN abbreviated code) or F (with a user-supplied PRED
      or ERROR code) will be set to a (conditional) likelihood. Upon
      simulation it will be ignored, and the DV data item will be set
      directly to the simulated value in abbreviated or user code.
      Also etas, if any, are understood to be population etas. Epsilon
      variables and the $SIGMA record may not be used. The L2 data
      item may not be used. The CONTR and CCONTR options of the $SUB-
      ROUTINES record may not be used. NONMEM cannot obtain the ini-
      tial estimate for omega. If the data are population, and MAXE-
      VALS=0 is not coded, then METHOD=1 LAPLACE is required. Compare
      with PREDICTION option.

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

Empower your Pipeline

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From: Bob Leary <Bob.Leary_at_certara.com>
Sent: Saturday, February 20, 2016 8:45 AM
To: Mark Sale; nmusers_at_globomaxnm.com
Subject: RE: Mixture model with logistic regression

This sounds like a good case for a nonparametric method - if you use the o=
ne in NONMEM, you might try
expanding Omega to counter shrinkage. The versions in USC*PACK and PHOENI=
X NLME optimize over
both support point positions and probabilities, so this is not necessary wi=
th those methods.
From: owner-nmusers_at_globomaxnm.com [owner-nmusers_at_globomaxnm.com] on behalf=
 of Mark Sale [msale_at_nuventra.com]
Sent: Friday, February 19, 2016 4:30 PM
To: nmusers_at_globomaxnm.com
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.
2525 Meridian Parkway, Suite 280
Research Triangle Park, NC 27713
Office (919)-973-0383

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Received on Sat Feb 20 2016 - 09:07:06 EST

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