Re: Genotype data missing in some individuals

Dear SoJeong, First you might want to answer the question whether that phenotype is indeed important in your dataset. With the initial popPK model you could plot posthoc clearance against bodyweight and/or inspect the posthocs of clearance for evidence of multiple peaks in your distribution. You also may see the impact of phenotype in stratified concentration versus time plots. Depending on the dataset, with its sampling scheme, number of subjects (perhaps a low number) and distribution across age, it could be masked. If the impact is clear however, it might be benificial to try to include the subjects wih missing genotype. With a clear effect, you might be able to develop a mixture model. The mixture approach would describe the different populations in your dataset corresponding to the different phenotypes. The genotype would than inform the mixture as a covariate - the missing information would fall back to the pure mixture approach. As a warning, this approach is quite difficult. I would advise you to read up on the nonmem guides ($MIX) on this and look in the literature for examples - the Karlsson group has published about it, most recently this one (it contains code): http://link.springer.com/article/10.1208/s12248-009-9093-4. A search in the literature gives you additional background such as http://www.page-meeting.org/pdf_assets/9595-PAGE2007_3.pdf and http://link.springer.com/article/10.1007/s10928-006-9038-9. If the impact is not clear, a more empirical approach might be called for, in this case a subset analysis, i.e. where you exclude the missing subjects, of the covariate relationship might be all that you could achieve. If there is no impact at all, you do not need the genotype of course. Hope this helps! Best regards, Jeroen http://pd-value.com [email protected] @PD_value +31 6 23118438 -- More value out of your data!