Re: Time-varing covariate and renal function as a covariate
Matt,
Douglas (like me) thinks about this as biological problem. I believe that 'size' is a continuum that incorporates at least some partition of weight into fat free mass (FFM) and fat mass (see Anderson & Holford 2009). The distinction between 'obese' and 'non-obese' is an artificial distinction used by health lobby groups, epidemiologists et al.
CLcr is predicted from creatinine production rate (CPR) divided by serum creatinine (Cockcroft & Gault for adults, various Schwartz methods of babies and children). CPR is predicted empirically using for age, sex and total body weight (TBW)(Schwartz uses height). Note that C&G is based on TBW. Other attempts to use IBW, LBW etc instead of TBW are imaginative at best but not based on data. Given an estimate of CPR by C&G method or by direct measurement of urinary excretion rate then CLcr can be computed under the assumption that serum creatinine (Scr) is at steady state (or with a somewhat different approach non-steady state Scr may be used). Thus the prediction of CLcr may or may not involve the use of weight. At this point it doesn't matter whether weight was used to calculate it or not. What is important is to recognize that both C&G and direct measurement both predict CLcr that is size dependent. In order to get around this confounding with allometric approaches to size you just need to standardize the CLcr to a standard weight. Then renal function can be calculated as a dimensionless quantity by dividing the weight standardized CLcr prediction by whatever CLcr you think is 'normal' for that weight. If you go through this quite simple process you can use renal function as a size independent covariate.
The time varying issue is then related to weight for calculation of CPR. If weight changes are not due to changes in muscle mass then C&G CLcr should be calculated from the baseline TBW because changes in TBW from baseline will not be associated with a change in CPR.
The time varying issue of size (e.g. based on FFM + Fat Mass) also depends on thinking about why weight is changing. If height is constant then formulae for predicting FFM will partition some of the change in TBW to FFM and some to Fat Mass. But if you weight is changing with some treatment that mainly affects Fat Mass then you might want to use baseline FFM and put the change in weight on the Fat Mass component.
The use of size for allometric scaling may also require some extra thinking about why the weight is changing. Volume of distribution may be sensitive to changes in weight due to fluid loss or accumulation but this probably won't affect clearance. On the other hand weight changes associated with growth in children seem to fit nicely with allometric theory because allometric size predicts the same clearance in children and adults (once maturation has been accounted for) (e.g. see Holford, Ma, Anderson 2012).
Best wishes,
Nick
1. Anderson BJ, Holford NHG. Mechanistic basis of using body size and maturation to predict clearance in humans. Drug Metab Pharmacokinet. 2009;24(1):25-36. 2. Holford NH, Ma SC, Anderson BJ. Prediction of morphine dose in humans. Paediatr Anaesth. 2012;22(3):209-22.
Quoted reply history
On 4/09/2013 11:20 a.m., Eleveld, DJ wrote:
> Hi Matt and Everyone,
>
> Whether or not "just using weight and CLCR should be enough" depends on whether
> you think that people who lost weight because of a drug (the formerly obese) are
> physilogically the same (with respect to the drugs in question) as those who were never
> obese. Are the formerly-obese maybe even a third group, different from non-obese and
> currently-obese?
>
> That said, making a distinction between healthy vs obese is odd. Obesity is a continuum and there
> shouldn't be model discontinuity when someone meets some arbitrary criteria. There shouldn't be
> choice "obese vs. non-obese", everything just comes as a smooth function of the
> covariates. Nature is "smooth" and, if possible, our models should be too.
>
> Warm regards,
>
> Douglas Eleveld
>
> -----Oorspronkelijk bericht-----
> Van: [email protected] [mailto:[email protected]] Namens
> Matt Hutmacher
> Verzonden: September 3, 2013 7:00 PM
> Aan: 'Nick Holford'; 'nmusers'
> Onderwerp: 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]>
>
> ________________________________
>