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From: Mark Sale <msale_at_nuventra.com>

Date: Fri, 19 Feb 2016 22:30:12 +0000

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

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

msale_at_nuventra.com<msale_at_kinetigen.com>

www.nuventra.com<http://www.nuventra.com>

Received on Fri Feb 19 2016 - 17:30:12 EST

Date: Fri, 19 Feb 2016 22:30:12 +0000

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

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

msale_at_nuventra.com<msale_at_kinetigen.com>

www.nuventra.com<http://www.nuventra.com>

Received on Fri Feb 19 2016 - 17:30:12 EST