Re: 答复: A question about handling large-scale data
Hi Dongyang,
another thing you might try besides ADVAN13 and SAEM is a log
transformation of the differential equations
dA/dt = g(x)
d exp(log(A)) /dt = g(x)
(d log(A) /dt) exp(log(A)) = g(x)
d log(A) /dt = g(x)/exp(log(A))
setting
log(A) = B
dB/dt = g(x)/exp(B)
then you can use your log-space observations and have
Y = B + EPS(1)
Regards
Sven
Quoted reply history
2014-11-21 8:58 GMT+01:00 Liudongyang_hotmail <[email protected]>:
> 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 didn’t work. Hope to get more suggestions
> from you and other users.
>
> THANKS IN ADVANCE!
>
>
>
> Cheers,
>
> Dongyang
>
>
>
> *发件人**:* [email protected] [mailto:[email protected]]
> *代表 *Nick Holford
> *发送时间:* 2014年11月20日 15:40
> *收件人:* Liudongyang_hotmail; [email protected]
> *主题:* 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: [email protected]
>
> 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, Trocóniz 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.
>
>