[NMusers] : [NMusers] A question about handling large-scale data

From: Liudongyang_hotmail <liudongyang.cp_at_hotmail.com>
Date: Fri, 21 Nov 2014 15:58:52 +0800

Dear Nick,

 

  THANKS for your quick suggestion. Because I want to construct a
mechanism-based model, H+ concentration has to be considered, which =
allows
us to receive circadian rhythm and dilution effect by food. The
log-transformation could be helpful. My current error model is Y =
LOG(IPRED)+EPS(1) for log-transformed DV value. Now, just rounding =
error
or termination warnings show up. My question is if it is a =
problem when
IPRED is large-scaled. If yes, how to handle it? And which kind of =
weighting
factor should be preferred for Log error model?

  By the way, the model incorporated IDR, circadian rhythm, and food
effects. Although it simulated data well, but fitting is in problem. In
order to remove overparameter problem, I just opened 4 THETAs, 3 ETAs, =
and
1 EPS to be estimated. Even so, fitting can not be converged. Also, I =
tried
different ADVAN13 and SAEM, both didnt work. Hope to get more =
suggestions
from you and other users.

  THANKS IN ADVANCE!

 

Cheers,

Dongyang

 

: owner-nmusers_at_globomaxnm.com =
[mailto:owner-nmusers_at_globomaxnm.com]
Nick Holford
ʱ: 20141120 15:40
ռ: Liudongyang_hotmail; nmusers_at_globomaxnm.com
: Re: [NMusers] A question about handling large-scale data

 

Dear Dongyang,
Transforming H+ concentration to the pH scale seems a reasonable idea.
However I would not give much importance to warnings such as "rounding
error" or "termination". You should evaluate your model based on
appropriate changes in objective function value, plausible parameters,
bootstrap confidence intervals of parameters and visual predictive =
checks of
the model predictions.
If you get errors associated with "numerical difficulty" then try a
different DE solver e.g. ADVAN13 or switch from FOCE to SAEM.
Best wishes,
Nick

On 20/11/2014 6:41 p.m., Liudongyang_hotmail wrote:

Hello All Nonmem Users,

 

  I am modeling intra-gastric H+ concentrations as PD biomarker, which
varies from 10e-7 to 10e-1. I log-transformed original data and used =
Y =
LOG(IPRED)+EPS(1) as log-linear error model firstly. The profile =
could be
simulated well, but when I fitted data, error messages as rounding =
error
or numerical difficulty showed up. =
Fitting was terminated
generally. Will anybody share their experiences or tips on this kind of
data?

  MANY THANKS IN ADVANCE!

 

Cheers,

Dongyang Liu, Clinical Pharmacologist

Phase I Unit, Clinical Pharmacology Research Center,

Peking Union Medical College Hospital, Beijing, China

M.P.: +86-18610966092

O.P.: +86-10-69158356

 

 

 

 





--
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
email: n.holford_at_auckland.ac.nz
http://holford.fmhs.auckland.ac.nz/
 
Holford SD, Allegaert K, Anderson BJ, Kukanich B, Sousa AB, Steinman A,
Pypendop, B., Mehvar, R., Giorgi, M., Holford,N.H.G. Parent-metabolite
pharmacokinetic models - tests of assumptions and predictions. Journal =
of
Pharmacology & Clinical Toxicology. 2014;2(2):1023-34.
 
Ribba B, Holford N, Magni P, Trocniz I, Gueorguieva I, Girard P, =
Sarr,C.,
Elishmereni,M., Kloft,C., Friberg,L. A review of mixed-effects models of
tumor growth and effects of anticancer drug treatment for population
analysis. CPT: pharmacometrics & systems pharmacology. 2014;Accepted
15-Mar-2014.

Received on Fri Nov 21 2014 - 02:58:52 EST

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