Re: Time-varing covariate and renal function as a covariate
Hans,
Thanks for pointing out the scientifically obvious that the empirical descriptive statisticians seem to be unaware of (your Minor Remark 1).
The same inability to think about the science can be found in this MDRD eGFR formula:
http://en.wikipedia.org/wiki/Renal_function
where the estimated exponent of -0.999 is proposed instead of the theoretically obvious value of -1.
Why is this theoretically obvious? Because clearance=rate elimination * conc^-1
So the correct exponent for serum creatinine is -1.
Best wishes,
Nick
Quoted reply history
On 6/09/2013 1:15 p.m., J.H. Proost wrote:
> Dear Joe,
>
> Thank you for your reply. You are right that one can model renal clearance using SeCr as you described. In fact, your approach is number three, after Nick's and mine (the order is purely arbitrary). It would be interesting to see real life examples and Monte Carlo simulations to see the performance of these approaches, which makes sense from a mechanistic / biological point of view.
>
> Two minor remarks:
>
> 1) THETA(2) may be fixed to 1, since renal clearance will be inversely related to SeCr. Of course, THETA(2) may be estimated, but a value too far from 1 would be suspicious. 2) to keep the same format as for other covariates, I suggest to put SECR in the numerator
>
> TVCL = THETA(1)*(SECR/STDCR)**THETA(2)
>
> and using a negative value for THETA(2) (-1).
>
> > How would you suggest smoothing is performed between Schwartz and C-G methods?
>
> This can be achieved by the following procedure. The Cockcroft&Gault equation can be used for an age of 18 and older; the Schartz equation can be used for the age less than 20. Over the range 18-20 years, both equations can be used. The logical choice is to use the interpolated value, so:
>
> CLcr(combined) = p * CLcr(C&G) + (1-p) * CLcr(Schwartz)
>
> where p = (age - 18) / (20 - 18)
>
> This guarantees a smooth relationship between age and CLcr, using both equations in their valid range. Even for equations that do not have an overlapping age range, such a range could be created by some minor extension of the ranges, e.g. by one year each; to cite Douglas: 'Nature is "smooth" and, if possible, our models should be too.'
>
> Please note that a really smooth profile is obtained only if age is calculated from the current date and date of birth, using 'decimal' years.
>
> DAYS360('date of birth','current date')/360
>
> If you are interested, I can send you a simple spreadsheet.
>
> best regards,
>
> Hans
>
> 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]
>
> ----- Original Message ----- From: "Standing Joseph (GREAT ORMOND STREET HOSPITAL FOR CHILDREN NHS FOUNDATION TRUST)" < [email protected] > To: "J.H.Proost" < [email protected] >; "Matt Hutmacher" < [email protected] >; "'Nick Holford'" < [email protected] >; "'nmusers'" < [email protected] >
>
> Sent: Wednesday, September 04, 2013 3:17 PM
>
> Subject: RE: [NMusers] Time-varing covariate and renal function as a covariate
>
> Dear Hans,
>
> If you are estimating GFR with C-G then you already have age (along with weight, sex and SeCr). Standardising SeCr is easy, for example:
>
> TVCL = THETA(1)*(STDCR/SECR)**THETA(2)
>
> where STDCR is the typical value of SeCr for that age (and/or sex in adults). You can find values for expected SeCr ranges for age usually reported alongside the measured level, from which you can take the mean or median as STDCR, or you could just use a published value. In adults STDCR differs between men and women, not so in children (the grey area of adolescence requires an extrapolation - see Johansson et al). If you are feeling particularly flashy you might want to use a published equation for predicting STDCR with age in children, like the Ceriotti 2008 model, that even goes down then up to account for maternal creatinine:
>
> STDCR = -2.37330-12.91367*LOG(AGE)+23.93581*AGE**0.5 ; Mean SeCr, age adjusted (F. Ceriotti et al, Clinical Chemistry 54:3 559-566 (2008))
>
> Another excellent paper where this method was used:
>
> Hennig S et al, Clin Pharmacokinet. 2013;52(4):289-301.
>
> How would you suggest smoothing is performed between Schwartz and C-G methods?
>
> Best wishes,
>
> Joe
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