Re: Missing Gender (Categorical values)

From:Alan Xiao Subject:Re: [NMusers] Missing Gender (Categorical values) Date:Wed, 31 Jul 2002 14:08:22 -0400 In methodology, I think you are right. However, in implementation, I'm not sure. As you know, to estimate PK parameters from sparse data, an appropriate model structure is a prerequisite although this model structure can be tested/selected using statistical tools. Similarly, in filling missing data, an appropriate model structure (or algorithm or whatever you name) is also a prerequisite. Likelihood method is just a statistic tool to force the model to fit the data (or converge) in a certain way and it is not the model itself. When the above two processes or tasks (filling the missing data and estimating PK parameters) are not correlated, that would be relatively simple - both model structures (or algorithms) can be adjusted independently at the same time based on the selected statistic tools. If they are correlated, how to make sure both model structures are correct or correctly adjusted based on a selected statistic tool is somehow a question I would like to ask. I'm just wondering if there are any reports on this. I noticed that in Mould et al's paper on topotecan (J Pharmacokinet Biopharm 1998;26(2):207-46), WEIGHT was used as a "built-in" covariate in the model. SEX was not identified as a covariate. I'm not sure whether SEX is really not significant or just because SEX is highly correlated with WEIGHT (equation 4) and the WEIGHT imputation covers the effect of SEX. Of course, I do not mean SEX must be in the model, either. I'm just curious if any testing was performed on this. Alan.