Imputation of missing sex covariate
From:VPIOTROV@PRDBE.jnj.com
Subject: [NMusers] Imputation of missing sex covariate
Date:Thu, 22 Aug 2002 12:31:48 +0200
During the recent discussion about missing sex covariate (see 99jul302002 "Missing Gender (Categorical values)") someone suggested to use other
covariates like body size and serum creatinine (SCR) to predict sex. I explored very briefly
this opportunity and found that indeed sex can be predicted with a reasonable precision. I used
two data sets, one including Caucasians (N=401) and another one including Orientals (Japanese
to be exact, N=65). I fitted a logistic regression model using WT, BMI or BSA as predictors, and
additionally, WT+SCR, BMI+SCR and BSA+SCR. I used an S-PLUS function glm() which
is a convenient tool for this task. It turned out BSA (calculated as WT^0.538*HT^0.396*0.0243)
was the best predictor (sex was predicted correctly in 71 % of Caucasians and in 63 %
of Orientals. BSA+SCR gave better precision (74% Caucasians and 82% Orientals).
Fitted equations are as follows:
Caucasians: logit = -9.184 + 4.754*BSA or
logit = -13.212 + 4.427*BSA + 4.541*SCR
Orientals: logit = -15.254 + 10.666*BSA or
logit = -13.242 + 7.861*BSA + 3.033*SCR
BSA in m2, SCR in mg/dL
How to use these equations for imputation: introduce BSA or BSA and SCR and calculate the logit.
If it is positive sex is male otherwise sex is female.
It makes no sense to use this approach if sex is missing in a high proportion of individuals, however,
if the proportion is relatively low (say, <20 %) you can try to impute and test sex as a covariate
affecting PK parameters without omitting subjects with missing sex.
Best regards,
Vladimir
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Vladimir Piotrovsky, Ph.D.
Research Fellow, Advanced PK-PD Modeling & Simulation
Global Clinical Pharmacokinetics and Clinical Pharmacology (ext. 5463/151)
Johnson & Johnson Pharmaceutical Research &Development
Turnhoutseweg 30
B-2340 Beerse
Belgium