RE: "Connect the dots" approach for a time-varying covariate
I would interpolate the covariate outside of NONMEM. I think doing it
within NONMEM unnecessarily complicates matters. You could use a cubic
spline within each subject to get the value of the missing variable or
your could use PROC MI in SAS to impute the missing value using the
other time values as predictors in the imputation process.
pete bonate
Peter L. Bonate, PhD, FCP
Genzyme Corporation
Senior Director
Clinical Pharmacology and Pharmacokinetics
4545 Horizon Hill Blvd
San Antonio, TX 78229 USA
[EMAIL PROTECTED]
phone: 210-949-8662
fax: 210-949-8219
crackberry: 210-315-2713
Quoted reply history
________________________________
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
On Behalf Of Samtani, Mahesh [PRDUS]
Sent: Tuesday, January 15, 2008 3:53 PM
To: [email protected]
Subject: [NMusers] "Connect the dots" approach for a time-varying
covariate
Dear NMusers,
I wish to model a biomarker that is controlled by a time-varying
variable. The temporal pattern of this time-varying variable is
irregular which makes a parametric description of its profile somewhat
difficult. I am hoping to use a "connect the dots" approach for this
exercise i.e. linear interpolation for the time-varying variable in
between the observation. I believe there are suggestions on the users
net to modify the dataset to complete slopes and intercepts for each
time interval. I was wondering if there is a simpler way to compute the
linear interpolation on the fly within the control stream. Finally, the
complicating issue is that the biomarker of interest needs an
initialization of it's compartment since it doesn't start at zero.
I would greatly appreciate if someone has a code and example dataset for
such an exercise.
Thanking the group in advance...Mahesh