Re: Missing Gender (Categorical values)

From:Nick Holford Subject:Re: [NMusers] Missing Gender (Categorical values) Date:Wed, 31 Jul 2002 07:34:37 +1200 Atul, You probably are missing data on sex (not gender -- Kim JS, Nafziger AN. Is it sex or is it gender? Clin Pharmacol Ther 2000;68(1):1-3). If you are missing sex then I suggest you simulate it. You know from the existing data the probability of being female (PRFEM) so simply simulate the missing sex values e.g. if you use NONMEM: $SIM (20000625 NEW) (12345678 UNIFORM) SUBPROBLEMS=1 ONLYSIMULATION IF (ICALL.EQ.4.AND.SEX.LT.0) THEN ; assume missing SEX is coded < 0 CALL RANDOM(2,R) IF (R.GT.PRFEM) THEN SEX=1 ;male ELSE SEX=0 ;female ENDIF ENDIF An alternative, more elegant approach, is to treat SEX as another DV. This is a bit trickier as it requires a LIKELIHOOD model that allows you to estimate continuous and categorical data at the same time. The missing SEX values are then predicted from the parameter describing the probability of being female just like you can predict DV values at times when you have no observations. Nick Nick Holford, Divn Pharmacology & Clinical Pharmacology University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand email:n.holford@auckland.ac.nz tel:+64(9)373-7599x6730 fax:373-7556 http://www.health.auckland.ac.nz/pharmacology/staff/nholford/