Re: Missing Gender (Categorical values) - my $0.02

From:"Lewis B. Sheiner" Subject: Re: [NMusers] Missing Gender (Categorical values) - my $0.02 Date:Wed, 31 Jul 2002 08:58:31 -0700 And just to chime in ... If you *must* know the effect of sex for some reason, then mult imputation is a way of evaluating what Diane (& Nick) call the likelihood option; actually a marginal likelihood -- p(Y|data) = Integral[p(Y,S|data),p(S|data)dS], where Y is your usual response, and S is sex. It is important to realize that both methods are attempting to find the MLE of the SAME the same underlying likelihood (i.e. model). They are simply using different methods to do so. The std error of the sex covaraite (when correctly computed using either method) will of course be larger than if there had been no missing data (you can't get something for nothing). On the other hand, if there is no reason to need to knbow the sex coefficient per se (e.g. you're just on a hunt for explanatory variables & don'tcare which ones you find), then you can just leave sex out if you ahve little informationon it, UNLESS the missingness of sex is non-ignorable (that is, the sex covariate is missing selectively in individuals whose responses are systematically different than the rest). In that case (which should be revealed by Leonid's analysis using a separate 'missing' class for those with missing sex data), if the other covariates do correlate with sex, then sex should be taken into account in the likelihood to avoid bias. This can be done using either of the computational approaches above. LBS. _/ _/ _/_/ _/_/_/ _/_/_/ Lewis B Sheiner, MD (lewis@c255.ucsf.edu) _/ _/ _/ _/_ _/_/ Professor: Lab. Med., Biopharmaceut. Sci. _/ _/ _/ _/ _/ Box 0626, UCSF, SF, CA, 94143-0626 _/_/ _/_/ _/_/_/ _/ 415-476-1965 (v), 415-476-2796 (fax)