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. (tm) 2525 Meridian Parkway, Suite 280 Research Triangle Park, NC 27713 Office (919)-973-0383 ms...@nuventra.com<ms...@kinetigen.com> www.nuventra.com<http://www.nuventra.com>
