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From: Matts Kågedal <mattskagedal_at_gmail.com>

Date: Sat, 20 Feb 2016 11:44:39 -0800

Hi Mark,

The pattern you see in the posthocs could possibly be a shrinkage

phenomenon. I.e. patients with AE most of the time will have the same ETA,

while patients 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

improvement with a mixture model. Before you reject your original model I

would therefore 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 when you simulate based on a normal distribution.

Best,

Matts Kågedal

Pharmacometrics, Genentech.

On Fri, Feb 19, 2016 at 2:30 PM, Mark Sale <msale_at_nuventra.com> wrote:

*> 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 thi=
*

s

*> 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, the=
*

re

*> 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 h=
*

oc

*> 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, sti=
*

ll

*> 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 distributio=
*

n

*> (and OMEGA), and it still gave me a very small difference in the intercep=
*

t

*> 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
*

*>
*

*> msale_at_nuventra.com <http://msale@kinetigen.com>
*

*>
*

*> www.nuventra.com
*

*>
*

*>
*

*>
*

*>
*

Received on Sat Feb 20 2016 - 14:44:39 EST

Date: Sat, 20 Feb 2016 11:44:39 -0800

Hi Mark,

The pattern you see in the posthocs could possibly be a shrinkage

phenomenon. I.e. patients with AE most of the time will have the same ETA,

while patients 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

improvement with a mixture model. Before you reject your original model I

would therefore 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 when you simulate based on a normal distribution.

Best,

Matts Kågedal

Pharmacometrics, Genentech.

On Fri, Feb 19, 2016 at 2:30 PM, Mark Sale <msale_at_nuventra.com> wrote:

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ll

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t

Received on Sat Feb 20 2016 - 14:44:39 EST