Re: Not enough sex!
From:Leonid Gibiansky
Subject:Re: [NMusers] Not enough sex!
Date:Tue, 30 Jul 2002 16:53:10 -0400
I would try the following approaches:
1. Create a three-level covariate:
gender= M, F, missing.
Then patients with gender="missing" should have intermediate values of the
parameters comparing with M and F. At least, this should give a feeling on
whether this covariate is important and what is the difference between the
parameters for M and F.
2. It is likely that one can predict gender based on weight and height
(something else ?). Simple tree model in S+ or any other similar software
can do it (find the model based on 30% of the available data and predict
for the other 70%). One can then fit the model with these "predicted"
gender and compare OF and fit with the model obtained in (1).
3. Alternative may be to try mixture model. If for 30% of patients with
known gender, the probability of being in one of two groups will correlate
with the gender then one may conclude that groups are defined by the
gender. If on the other hand, the mixture model will not reveal importance
of gender (again, comparing model (3) with (1) and (2) ), then one can
safely ignore the issue and omit the gender. In fact, weight and height
may compensate for absence of gender.
On the other hand, gender is one of the most easily measured covariates. It
should be possible to recover it if any information about the study is
available.
Leonid