From: Ribbing, Jakob <*Jakob.Ribbing*>

Date: Tue, 13 Jan 2009 00:50:09 -0000

Leonid,

I usually prefer multiplicative parameterisation as well, since it is

easier to set boundaries (which is not necessary for power models, but

for multiplicative-linear models). However, boundaries on the additive

covariate models can still be set indirectly, using EXIT statements (not

as neat as boundaries directly on the THETAS, I admit).

In this case it may possibly be more mechanistic using the additive

parameterisation: For example if the non-renal CL is mainly liver, the

two blood flows run in parallel and the two elimination processes are

independent (except there may be a correlation between liver function

and renal function related to something other than size). A

multiplicative parameterisation contains an assumed interaction which is

fixed and in this case may not be appropriate. If the drug is mainly

eliminated via filtration, why would two persons, with WT 50 and 70 kg

but otherwise identical (including CRCL and any other covariates, except

WT), be expected to differ by 36% in CL? This is what you get using a

multiplicative parameterisation. The fixed interaction may also drive

the selection of the functional form (e.g. a power model vs a linear

model for CRCL on CL). I do not know anything about Peter's specific

example so this is just theoretical.

Regarding 3 below, is the suggestion to estimate independent allometric

models on CL for each level of renal function?

Thanks

Jakob

-----Original Message-----

From: owner-nmusers

On Behalf Of Leonid Gibiansky

Sent: 12 January 2009 23:30

To: Bonate, Peter

Cc: nmusers

Subject: Re: [NMusers] CrcL or Cr in pediatric model

Hi Peter,

If allometric exponent is fixed, collinearity is not an issue from the

mathematical point of view (convergence, CI on parameter estimates,

etc.). However, in this case CRCL can end up being significant due to

additional WT dependence (that could differ from allometric) rather than

due to renal function influence (that is not good if you need to

interpret it as the renal impairment influence on PK).

Few points to consider:

1. I usually normalize CRCL by WT^(3/4) or by (1.73 m^2 BSA) to get

rid of WT - CRCL dependence. If you need to use it in pediatric

population, normalization could be different but the idea to normalize

CRCL by something that is "normal CRCL for a given WT" should be valid.

2. In the pediatric population used for the analysis, are there any

reasons to suspect that kids have impaired renal function ? If not, I

would hesitate to use CRCL as a covariate.

3. Often, categorical description of renal impairment allows to

decrease or remove the WT-CRCL correlation

4. Expressions to compute CRCL in pediatric population (note that

most of those are normalized by BSA, as suggested in (1)) can be found

here:

http://www.globalrph.com/specialpop.htm

http://www.thedrugmonitor.com/clcreqs.html

5. Couple of recent papers:

http://www.clinchem.org/cgi/content/full/49/6/1011

http://www.ajhp.org/cgi/content/abstract/37/11/1514

Thanks

Leonid

P.S. I do not think that this is a good idea to use additive dependence:

TVCL=THETA(X)*(WT/70)**0.75+THETA(Y)*CRCL

--------------------------------------

Leonid Gibiansky, Ph.D.

President, QuantPharm LLC

web: www.quantpharm.com

e-mail: LGibiansky at quantpharm.com

tel: (301) 767 5566

Bonate, Peter wrote:

*> I have an interesting question I'd like to get the group's collective
*

*> opinion on. I am fitting a pediatric and adult pk dataset. I have
*

*> fixed weight a priori to its allometric exponents in the model. When
*

I

*> test serum creatinine and estimated creatinine clearance equation as
*

*> covariates in the model (power function), both are statistically
*

*> significant. CrCL appears to be a better predictor than serum Cr (LRT
*

=

*> 22.7 vs 16.7). I have an issue with using CrCL as a predictor in the
*

*> model since it's estimate is based on weight and weight is already in
*

*> the model. Also, there might be collinearity issues with CrCL and
*

*> weight in the same model, even though they are both significant. Does
*

*> anyone have a good argument for using CrCL in the model instead of
*

serum Cr?

*>
*

*> Thanks
*

*>
*

*> Pete bonate
*

*>
*

*>
*

*>
*

*> Peter L. Bonate, PhD, FCP
*

*> Genzyme Corporation
*

*> Senior Director
*

*> Clinical Pharmacology and Pharmacokinetics
*

*> 4545 Horizon Hill Blvd
*

*> San Antonio, TX 78229 USA
*

*> _peter.bonate *

*> phone: 210-949-8662
*

*> fax: 210-949-8219
*

*> crackberry: 210-315-2713
*

*>
*

*> alea jacta est - The die is cast.
*

*>
*

*> Julius Caesar
*

*>
*

*> *

Received on Mon Jan 12 2009 - 19:50:09 EST

Date: Tue, 13 Jan 2009 00:50:09 -0000

Leonid,

I usually prefer multiplicative parameterisation as well, since it is

easier to set boundaries (which is not necessary for power models, but

for multiplicative-linear models). However, boundaries on the additive

covariate models can still be set indirectly, using EXIT statements (not

as neat as boundaries directly on the THETAS, I admit).

In this case it may possibly be more mechanistic using the additive

parameterisation: For example if the non-renal CL is mainly liver, the

two blood flows run in parallel and the two elimination processes are

independent (except there may be a correlation between liver function

and renal function related to something other than size). A

multiplicative parameterisation contains an assumed interaction which is

fixed and in this case may not be appropriate. If the drug is mainly

eliminated via filtration, why would two persons, with WT 50 and 70 kg

but otherwise identical (including CRCL and any other covariates, except

WT), be expected to differ by 36% in CL? This is what you get using a

multiplicative parameterisation. The fixed interaction may also drive

the selection of the functional form (e.g. a power model vs a linear

model for CRCL on CL). I do not know anything about Peter's specific

example so this is just theoretical.

Regarding 3 below, is the suggestion to estimate independent allometric

models on CL for each level of renal function?

Thanks

Jakob

-----Original Message-----

From: owner-nmusers

On Behalf Of Leonid Gibiansky

Sent: 12 January 2009 23:30

To: Bonate, Peter

Cc: nmusers

Subject: Re: [NMusers] CrcL or Cr in pediatric model

Hi Peter,

If allometric exponent is fixed, collinearity is not an issue from the

mathematical point of view (convergence, CI on parameter estimates,

etc.). However, in this case CRCL can end up being significant due to

additional WT dependence (that could differ from allometric) rather than

due to renal function influence (that is not good if you need to

interpret it as the renal impairment influence on PK).

Few points to consider:

1. I usually normalize CRCL by WT^(3/4) or by (1.73 m^2 BSA) to get

rid of WT - CRCL dependence. If you need to use it in pediatric

population, normalization could be different but the idea to normalize

CRCL by something that is "normal CRCL for a given WT" should be valid.

2. In the pediatric population used for the analysis, are there any

reasons to suspect that kids have impaired renal function ? If not, I

would hesitate to use CRCL as a covariate.

3. Often, categorical description of renal impairment allows to

decrease or remove the WT-CRCL correlation

4. Expressions to compute CRCL in pediatric population (note that

most of those are normalized by BSA, as suggested in (1)) can be found

here:

http://www.globalrph.com/specialpop.htm

http://www.thedrugmonitor.com/clcreqs.html

5. Couple of recent papers:

http://www.clinchem.org/cgi/content/full/49/6/1011

http://www.ajhp.org/cgi/content/abstract/37/11/1514

Thanks

Leonid

P.S. I do not think that this is a good idea to use additive dependence:

TVCL=THETA(X)*(WT/70)**0.75+THETA(Y)*CRCL

--------------------------------------

Leonid Gibiansky, Ph.D.

President, QuantPharm LLC

web: www.quantpharm.com

e-mail: LGibiansky at quantpharm.com

tel: (301) 767 5566

Bonate, Peter wrote:

I

=

serum Cr?

Received on Mon Jan 12 2009 - 19:50:09 EST