RE: Time-varing covariate and renal function as a covariate
Dear Matt,
Your first hypothetical scenario is an argument not to use CRCL because as you
point out, weight is entering the model twice: once to predict renal function
and once to scale for size. Other problems of using models that predict CLCr
(when really you are interested in your drug CL, not the CL of endogenous
creatinine) is that they are only valid for certain populations so if you have
say adults and children in your dataset, at what point do you switch between
C-G and the Schwartz method? Perhaps a better way is to scale clearance with
measured creatinine normalised to the age-adjusted value (which if your drug is
renally cleared to any extent should be correlated in some way), and then have
a separate weight scaling - that way age, weight and SeCr all only enter into
the model once, and can be updated as often as they are measured for
time-varying techniques. You can then try different metrics for weight if you
have some obese subjects (FFM, LBW...). This approach has been used a couple
of times:
Johansson ÅM et al. TDM. 2011;33(6):711-8.
Germovsek E et al. Age-Corrected Creatinine is a Significant Covariate for
Gentamicin Clearance in Neonates. PAGE 2013.
BW,
Joe
Joseph F Standing
MRC Fellow, UCL Institute of Child Health
Antimicrobial Pharmacist, Great Ormond Street Hospital
Tel: +44(0)207 905 2370
Mobile: +44(0)7970 572435
Quoted reply history
________________________________________
From: [email protected] [[email protected]] On Behalf Of
Matt Hutmacher [[email protected]]
Sent: 03 September 2013 18:00
To: 'Nick Holford'; 'nmusers'
Subject: RE: [NMusers] Time-varing covariate and renal function as a covariate
Hello Nick, Hans
Thanks for the replies and sorry for being so vague. I wanted to get your
opinions about such a scenario without providing information that might
"steer" the dialogue.
Perhaps a hypothetical will help clarify my scientific curiosity. Let's say
at baseline we take measurements of weight (WT), etc. Assume
Cockcroft-Gault is used to predict CLCR. We formulate a model either by
Nick's or Hans' method below to relate WT and CLCR (a function of WT) to CL
of the drug. However, over time, the drug changes WT... in some way the
ratio of fat to lean mass is altered by the drug. Should we expect the same
structural relationship to hold as we would have assumed at baseline (before
we knew the drug changes WT)? And if so, then should we assume the same
coefficients (exponents) for CLCR and WT would hold over time in such a
model, such that just adjusting CLCR and WT as time varying covariates is
all that is needed to be predictive? As another example, let's assume we do
a pooled population PK using healthy volunteers and obese patients. Then,
say a drug is administered that reduces WT. Should we use the same exponent
(coefficient) for healthy volunteers and obese patients? And if the drug
works, should at what point should we treat the obese patients as healthy
volunteers - or would just using WT and CLCR take care of it?.
Best regards,
Matt
-----Original Message-----
From: [email protected] [mailto:[email protected]] On
Behalf Of Nick Holford
Sent: Monday, September 02, 2013 12:16
To: 'nmusers'
Subject: Re: [NMusers] Time-varing covariate and renal function as a
covariate
Matt,
Thanks for your interest in this question. Hans and I have differing
approaches for including 'renal function' but I think we agree on 'size'.
Our differences of approach to 'renal function' are not very important for
those who understand the biology and pharmacology. But its different when we
have to talk to statisticians.
While I recognize that you are not typical of statisticians (you know
something about biology and pharmacology) it would help me (and probably
Hans) if you stated more precisely what you mean by 'renal function' and
'size' and why you think there is a challenge if weight changes over time?
Best wishes,
Nick
On 2/09/2013 9:04 a.m., J.H. Proost wrote:
> Dear Matt,
> I'm not quite sure that I fully understand your question. I would say
> that a changing renal function and a changing weight over time can be
> handled as described earlier by Nick Holford, or by the modified
> approach I suggested. An important point is how to express renal
> function.
> Nick's method implies that 'size' should be excluded from 'renal
> function', so CLCR needs to be normalized / standardized, e.g. using
> CLCR in ml/min/1.73m2. Now, CLCR is a 'pure' measure of the kidney
> function (of course, we know that its precision is rather poor, but
> that is a different topic, interesting as well!). The factor
> WEIGHT^0.75 deals with the factor 'size'. This approach treats CLCR as
> a covariate similar to other covariates, making it more suitable for a
> standardized approach for covariate analysis.
> In the approach proposed by me, CLCR should be the 'individual's renal
> clearance of creatinine', so it should expressed in ml/min (or
> converted to e.g. l/h), and it should not be normalized /
> standardized. Here, CLCR includes both kidney function and size (in
> Nick's view a disadvantage, in my view an advantage), and the renal
> part of the equation does not need further modification to take 'size'
> into account. This approach treats CLCR as a 'special' covariate,
> directly related to the renal clearance of the drug. This may be
> advantageous for clinical purposes, e.g. dose calculation and
> therapeutic drug monitoring.
> In my view, both approaches have advantages and disadvantages.
> best regards,
> Hans Proost
> Johannes H. Proost
> Dept. of Pharmacokinetics, Toxicology and Targeting University Centre
> for Pharmacy Antonius Deusinglaan 1
> 9713 AV Groningen, The Netherlands
> tel. 31-50 363 3292
> fax 31-50 363 3247
> Email: [email protected] <mailto:[email protected]>
>
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