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
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
--
Nick Holford, Professor Clinical Pharmacology
Dept Pharmacology & Clinical Pharmacology, Bldg 503 Room 302A
University of Auckland,85 Park Rd,Private Bag 92019,Auckland,New Zealand
office:+64(9)923-6730 mobile:NZ +64(21)46 23 53 FR +33(7)85 36 84 99
email: [email protected]
http://holford.fmhs.auckland.ac.nz/
Holford NHG. Disease progression and neuroscience. Journal of Pharmacokinetics
and Pharmacodynamics. 2013;40:369-76
http://link.springer.com/article/10.1007/s10928-013-9316-2
Holford N, Heo Y-A, Anderson B. A pharmacokinetic standard for babies and
adults. J Pharm Sci. 2013:
http://onlinelibrary.wiley.com/doi/10.1002/jps.23574/abstract
Holford N. A time to event tutorial for pharmacometricians. CPT:PSP. 2013;2:
http://www.nature.com/psp/journal/v2/n5/full/psp201318a.html
Holford NHG. Clinical pharmacology = disease progression + drug action. British
Journal of Clinical Pharmacology. 2013:
http://onlinelibrary.wiley.com/doi/10.1111/bcp.12170/abstract
