答复: A question about handling large-scale data

2 messages 2 people Latest: Nov 21, 2014

答复: A question about handling large-scale data

From: Liu Dongyang Date: November 21, 2014 technical
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
Quoted reply history
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.
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. > >