RE: Coding for missing data values

From: Nick Holford Subject: RE: [NMusers] Coding for missing data values Date: Tue, June 29, 2004 9:42 pm Bill, Thanks for explaining your approach. I agree with your overall strategy (not very Holfordesque!) if you do not want to use the joint modelling method to describe the missing covariate. Returning to being Holfordesque, I would quibble with the choice of an additive model for all covariate effects. Unless one is careful these kinds of models can lead to predictions of negative values which are usually unphysiological. I prefer to use multiplicative covariate models for empirical covariate effects e.g. POPCL=THETA(1) KMISS=THETA(2) KTECL=THETA(2) KCOVN=THETA(4) IF(TECL.EQ.0) THEN GRPCL=POPCL*EXP(KMISS)*EXP((COVn-x.x)*KCOVN)*EXP(...) ... ELSE GRPCL=POPCL*EXP((TECL-5.4)*KTECL)*EXP(COVn-x.x)*KCOVN)*EXP(...) ... ENDIF CL=GRPCL*EXP(ETA(1)) In this particular case the TECL is probably being used to predict renal function in which case an additive model would be mechanistically more appropriate. I would then prefer to write: PPCLNR=THETA(1) ; constrain this to be non-negative in $THETA KMISS=THETA(2) POPCLR=THETA(2) KCOVN=THETA(4) TCLSTD=5.4 ; or whatever value is appropriate for a standard renal function IF(TECL.EQ.0) THEN RF=EXP(KMISS) ; KMISS.NE.0 means Renal Function is non-standard when TECL is missing ELSE RF=TECL/TCLSTD ; RF.EQ.1 means this is standard Renal Function ENDIF GRPCL=(PPCLNR + RF*POPCLR)*EXP(COVn-x.x)*KCOVN)*EXP(...) ... CL=GRPCL*EXP(ETA(1)) Nick -- Nick Holford, Dept Pharmacology & Clinical Pharmacology University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand email:n.holford@auckland.ac.nz tel:+64(9)373-7599x86730 fax:373-7556 http://www.health.auckland.ac.nz/pharmacology/staff/nholford/ _______________________________________________________