Missing data values

From: Nick Holford <n.holford@auckland.ac.nz> Subject: Missing data values Date: Tue, 12 Sep 2000 09:21:18 +1200 Paul, There are 2 main kinds of approach available to you for dealing with missing covariates. The most commonly used is to replace the missing values with the median (or similar) of the non-missing values. You can do this in the data file or use a missing value code in the data file and use code in NM-TRAN to substitute the median eg IF (K.EQ.-1) THEN MYK=4.0 ELSE MYK=K ENDIF The other more sophisticated method is to impute the missing K value. Do this by putting all the non-missing K values in the DV column and add an extra column DVID which distinguishes the original DV from the DV which is a K value eg. 1=Original DV, 2=K. Then you can do this: MYK=THETA(1) + ETA(1); THETA(1) is pop median value for K; ETA(1) is its variability ;use MYK as you wish as covariate IF (DVID.EQ.1) THEN Y=F+EPS(1) ; or whatever you want for your original DV ELSE Y=MYK+EPS(2) ; FIX EPS(2) to a small value e.g. 0.0001 ENDIF The model for MYK can be as complex as you wish eg. if you think Na is a predictor of K you can put measured Na in a covariate in the MYK expression. See : Karlsson M, Jonsson E, Wiltse C, Wade J. Assumption testing in population pharmacokinetic models: illustrated with an analysis of moxonidine data from congestive heart failure patients. J Pharmacokinet Biopharm 1998;26(2):207-46. -- Nick Holford, Division of Pharmacology & Clinical Pharmacology University of Auckland, Private Bag 92019, 85 Park Road, Auckland, NZ email: n.holford@auckland.ac.nz tel:+64(9)373-7599x6730 fax:373-7556 http://www.phm.auckland.ac.nz/Staff/NHolford/nholford.htm