Re: [NMusers] "Connect the dots" approach for a time-varying covariate

Mahesh
This is the interpolation code for use in the $DES block, assuming that
you have values of a covariate at all observation records. If not, you
need to control the code by appropriate IF-statements or interpolate
(for observation times) at the time of data set creation:

    IF(NEWIND.NE.2) THEN ; SET UP FOR NEW INDIVIDUAL
      LCP=0
      LTM=TIME
    ENDIF
    INT = LCP ; INTERCEPT = LAST covariate value
    TINT = LTM ; TIME SCALE ORIGIN = LAST TIME
    DEN = TIME-TINT
    SLOP = 0
    IF(DEN.NE.0) SLOP = (CONC-INT)/DEN
; CONC is the value of the covariate in your case
    LCP=CONC ; RECORD CURRENT TIME & conc
    LTM=TIME ; FOR NEXT CALL

$DES
    NPUT=INT+SLOP*(T-TINT)
    ....

;INPUT IS INTERPOLATED covariate, can be used even in $DES block

Interpolation is better than imputation if you use differential
equations (and need values at any time, not only at observation times).
For example, with Nonmem interpolation, you can use interpolated PK and
effect compartment model (to integrate, you need values at all times)

Initialization can be done either using a unit dose to the biomarker
compartment and assigning bioavailability (Nonmem V and VI) or (with
Nonmem VI) using A_0(1)= etc. statements

Leonid

--------------------------------------
Leonid Gibiansky, Ph.D.
President, QuantPharm LLC
web: www.quantpharm.com
e-mail: LGibiansky at quantpharm.com
tel: (301) 767 5566




Samtani, Mahesh [PRDUS] wrote:
> 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
>
>
>
Received on Tue Jan 15 2008 - 18:26:40 EST