Bob,
That certainly makes sense, but that options seems to not be available in
NONMEM, using LIKE seems to require using FOCE LAPLACE
LIKELIHOOD
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
[email protected]<[email protected]>
www.nuventra.com<http://www.nuventra.com>
Empower your Pipeline
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________________________________
From: Bob Leary <[email protected]>
Sent: Saturday, February 20, 2016 8:45 AM
To: Mark Sale; [email protected]
Subject: RE: Mixture model with logistic regression
This sounds like a good case for a nonparametric method - if you use the one
in NONMEM, you might try
expanding Omega to counter shrinkage. The versions in USC*PACK and PHOENIX
NLME optimize over
both support point positions and probabilities, so this is not necessary with
those methods.
________________________________
From: [email protected] [[email protected]] on behalf of
Mark Sale [[email protected]]
Sent: Friday, February 19, 2016 4:30 PM
To: [email protected]
Subject: [NMusers] Mixture model with logistic regression
Has anyone every tried to use a mixture model with logistic regression? I have
data on a AE in several hundred patients, measured multiple times (10-20 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 ordinary
logistic regression, just glm in R, there is also a nice concentration effect
on the AE in addition). Running the usual logistic model, not surprisingly, I
get a really big ETA on the intercept, with 68% of the people having ETA small
negative, 25% ETA ~ 1 and 7% ETA ~ 10. No covariates seem particularly
predictive of the post hoc ETA. I thought I could use a mixture 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 the intercept. Even when I
FIX the OMEGA to a reasonable number, I still get essentially no one in the 2nd
and 3rd distribution. I tried fixing the fraction 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 just
using FOCE, Laplacian, without interaction, and LIKE.
Any ideas?
Mark
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
[email protected]<UrlBlockedError.aspx>
www.nuventra.com<http://www.nuventra.com>
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