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Re: Piece-wise PD model

From: Ekaterina Gibiansky <EGibiansky>
Date: Wed, 23 Jun 2010 11:20:34 -0400
Brendan,

The implementation of your piece-wise model is not correct. It should be

EFFECT=AEFF*SLOPE1
IF(
AEFF.GT.CHANGE) EFFECT=CHANGE*SLOPE1+(AEFF - CHANGE)*SLOPE2

Regards,
Katya

Ekaterina Gibiansky, Ph.D.
CEO&CSO, QuantPharm LLC
Web:     www.quantpharm.com
Email:   EGibiansky at quantpharm.com
Tel:     (301)-717-7032 

On 6/23/2010 10:20 AM, Brendan Johnson wrote:

NMusers,

In a somewhat similar theme to Hauke’s post, I am having an issue with conditional assignment statements in $ERROR.  I am trying to fit a piece-wise PD model for a KPD system. A linear PD model is ok, but saturating models, while I expect these to provide a better fit, converge with parameters that essentially replicate a linear model (tried Emax, Power, Exponential). My last ditch attempt was to try a piece-wise model, with 2 linear slopes, estimating the change point.

 

Something like this

 

$PK

CHANGE=THETA(.)

SLOPE1=THETA(.)

SLOPE2=THETA(.)

 

$ERROR

A(.)=AEFF                               ;amount in effect compartment

IF(AEFF.LT.CHANGE) THEN

SLOPE=SLOPE1

ELSE

SLOPE=SLOPE2

ENDIF

 

EFFECT=AEFF*SLOPE

 

I always find minimization is terminated early, and the gradient for CHANGE is zero at first iteration.  The gradient does have a value at subsequent iterations, but the final estimate of CHANGE (at termination anyway) is usually not far off the initial estimate…I suspect it is not actually being estimated, just floating a bit.  Even if I fix CHANGE to a reasonable value, I see minimization is terminated early.

 

Is there some trick I am missing here? or is it not possible to estimate a parameter within a conditional assignment statement in $ERROR?

(seems like you can do this in $PK when a covariate or time is used in the IF statement)

 

Thanks for any help,

Brendan Johnson

GlaxoSmithKline, RTP

 

 

 

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Received on Wed Jun 23 2010 - 11:20:34 EDT

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