Re: Missing covariates

From: LSheiner <lewis@c255.ucsf.edu> Subject: Re: Missing covariates Date: Mon, 02 Jul 2001 12:07:46 -0700 Of course that is fine, but it implies a different model than the original one. The marginal likelihood methods, i.e., the methods that integrate the likelihood over the distribution of the missing data -- all of those I discussed as "principled" -- assume that the SAME conditional (on the covariates) likelihood applies to all subjects. Thus, if you believe that CL differs between sexes (as in your example) then using the marginal likelihood, you essentially "assign" a fraction (equal to the probability of being male) of the likelihood from each individual with missing sex covaraite to the class of men, and the remaining fraction to the class of women. In your model, you create a third class which is neither male nor female, but "missing sex" (and introduce an additional parameter for their mean clearance), and do not demand that the clearance of this group be the probability weighted average of that for males and females. Other than increasing model complexity, this poses a problem for a causal or predictive model (as opposed to a descriptive model), namely How do you predict Cl for a new individual? If you know his/her sex, presumbaly you use the sex-specific value; if you don't, presumbaly you use the new 3rd class. Now, if the reasons for missingness are the same for the new patient as they were for those originally studied, this will be a reasonable empirical model (it is certainly not a mechanistic one, as it depends on a non-mechanistic covariate: missingness of data). If not, though, ... LBS. -- _/ _/ _/_/ _/_/_/ _/_/_/ Lewis B Sheiner, MD (lewis@c255.ucsf.edu) _/ _/ _/ _/_ _/_/ Professor: Lab. Med., Bioph. Sci., Med. _/ _/ _/ _/ _/ Box 0626, UCSF, SF, CA, 94143-0626 _/_/ _/_/ _/_/_/ _/ 415-476-1965 (v), 415-476-2796 (fax)