Re: Problem with estimating sigma when using M3 method

On 5/31/2023 8:58 AM, Hiba Sliem wrote: > Thank you! I followed your advice by ignoring bql values at first and I managed > to get my model to work with satisfying results. > > I did chose not to exclude any bql DV in my final dataset, including those > pre-first dose samples, since removing them only slightly changes my parameter > estimates while increasing the objective function, what's the usual approach > when dealing with predose observations? > > And finally while my model seems to be a good fit, during its execution nonmem > displayed several error messages in the output window that weren't reported in > the res file such as: > > OCCURS DURING SEARCH FOR ETA AT INITIAL VALUE, ETA=0 > PK SUBROUTINE: ERROR IN COMPUTATION > ATTEMPT TO COMPUTE BASE*POWER WITH BASE < 0 > PRED EXIT CODE= 1 > > OCCURS DURING SEARCH FOR ETA AT INITIAL VALUE, ETA=0 > ERROR In TRANS4 ROUTINE, CL (could also be V2 or Q) IS NEGATIVE > > All these errors involved the first subject ID=1 exclusively > > Should these messages be taken into consideration or can they be overlooked > considering they weren't transcribed in the report file and I'm satisfied with > my model? > > Thank you again > > regards > > -----Original Message----- > From: Leonid Gibiansky <[email protected]> > Sent: Tuesday, 30 May 2023 04:17 > To: Hiba Sliem <[email protected]> > Cc: nmusers <[email protected]> > Subject: Re: [NMusers] Problem with estimating sigma when using M3 method > > The code is incorrect, $ERROR should be > > $ERROR > LOQ=0.1 > IPRED = F > W1 = THETA(8)*IPRED > W2 = THETA(9) > W = SQRT(W1**2+W2**2) > IRES = DV - IPRED > IWRES = IRES/W > DUM = (LOQ -IPRED)/W > CUMD = PHI(DUM) > IF(BLQ.EQ.0) THEN > F_FLAG=0 > Y= IPRED +W1*ERR(1) + W2*ERR(2) > ELSE > F_FLAG=1 > Y=CUMD > MDVRES = 1 > ENDIF > > My be it makes sense to show the entire $PK and $EST blocks: easier to check. > Which ADVAN do you use? If ADVAN6, switch to ADVAN13 > > Use MATRIX=S on $COV step > Use NOABORT on $EST step > > try to use NSIG=4 SIGL=12 on $EST with LAPLACEAN > > For pre-dose samples, do you mean pre-first dose? Then those should be ignored, > removed from the data set, or use EVID=2 MDV=1, then they will be ignored at > estimation. After washout, if all samples are BQLs, I would ignore them in a > similar way, at least initially. > For washout setting, all versions are fine, You can use TIME=0 EVID=4 for the > first dose after washout. > > To check the code, I would first remove BQLs (use MDV=1 EVID=2 for those, to > get IPRED), use FOCEI rather than LAPLACEAN, and make sure that the model fit > is good. Then check that IPRED at the points of BQLs is small. If not, check > whether those BQLs are reasonable or could be data errors. Then switch to > LAPLACEAN with initial values of all parameters set at the final values of the > previous FOCEI run. Make sure you use INTERACTION options for all runs. > > Good luck! > Leonid > > On 5/29/2023 8:02 PM, Hiba Sliem wrote: > > > Hi > > > > Thank you for your assistance, unfortunately I still haven't found a solution > > to my issue, and I keep running into either one of these error messages > > depending on my dataset codification: > > > > #PROGRAM TERMINATED BY OBJ > > ERROR IN NCONTR WITH INDIVIDUAL 6 ID= 6.00000000000000E+00 > > NUMERICAL HESSIAN OF OBJ. FUNC. FOR COMPUTING CONDITIONAL ESTIMATE > > IS NON POSITIVE DEFINITE > > > > #R MATRIX ALGORITHMICALLY SINGULAR > > AND ALGORITHMICALLY NON-POSITIVE-SEMIDEFINITE 0R MATRIX IS OUTPUT > > 0COVARIANCE STEP ABORTED > > > > I finally managed to have both estimation and covariance not fail with the > > following error code : > > $ERROR > > LOQ=0.1 > > IPRED = F > > W1 = THETA(8)*IPRED > > W2 = THETA(9) > > IRES = DV - IPRED > > IWRES = IRES/(W1 + W2) > > DUM = (LOQ -IPRED)/(W1 + W2) > > CUMD = PHI(DUM) > > IF(BLQ.EQ.0) THEN > > F_FLAG=0 > > Y= IPRED +W1*ERR(1) + W2*ERR(2) > > ELSE > > F_FLAG=1 > > Y=CUMD > > MDVRES = 1 > > ENDIF > > > > $THETA > > (0.01, 0.38) ; [w1] > > (0.01, 0.1) ; [w2] > > > > $SIGMA > > 1 FIX ;[P] sigma(1,1) > > 1 FIX ;[P] sigma(2,2) > > > > However my results were accompanied by the following message: > > > > MINIMIZATION SUCCESSFUL > > HOWEVER, PROBLEMS OCCURRED WITH THE MINIMIZATION. > > REGARD THE RESULTS OF THE ESTIMATION STEP CAREFULLY, AND ACCEPT THEM ONLY > > AFTER CHECKING THAT THE COVARIANCE STEP PRODUCES REASONABLE OUTPUT. > > > > I think a part of the issue is the way I've been formatting my dataset, since I > > get different results depending on the way I've set it up, so I'd like to have > > your opinion on the best way to proceed in the following situations: > > >predose samples that are BLQ or higher at Time of dosing: > > ID TIME DV AMT BLQ > > 1 0 0.1 0 1 > > 1 0 0 10 0 > > > > Do I keep the original times or do something like that?: > > > > ID TIME DV AMT BLQ > > 1 -0.01 0.1 0 1 > > 1 0 0 10 0 > > > > And can I ignore these observations altogether (using something like MDV=100 > > for example)? > > > > > Washout period followed by a predose sample and a new administration of the > > > same drug (different dose): > > > > ID TIME DV AMT BLQ EVID > > 1 0 0 10 0 > > 1 > > 1 1 0.5 0 0 > > 0 > > 1 .... ... .... > > .... .... > > After washout > 1 1000 0.1 0 1 0 > > 1 1000 0 20 0 > > 1 > > > > Do I leave it inchanged, or use EVID4 either this way: > > ID TIME DV AMT BLQ EVID > > 1 0 0 10 0 > > 1 > > 1 1 0.5 0 0 > > 0 > > 1 .... ... .... > > .... .... > > 1 1000 0.1 0 1 > > 0 > > 1 0 0 20 0 > > 4 > > > > Or that way?: > > ID TIME DV AMT BLQ EVID > > 1 0 0 10 0 > > 1 > > 1 1 0.5 0 0 > > 0 > > 1 .... ... .... > > .... .... > > 1 0 0 20 0 > > 4 > > 1 0 0.1 0 1 > > 0 > > > > Sorry if these all seem like obvious questions, but I've been struggling to get > > satisfying results over the last few days and I'd like to understand what I've > > been doing wrong. > > > > Kind regards, > > > > -----Original Message----- > > From: Philip Harder Delff <[email protected]> > > Sent: Friday, 26 May 2023 21:42 > > To: Leonid Gibiansky <[email protected]> > > Cc: Hiba Sliem <[email protected]>; nmusers > > <[email protected]> > > Subject: Re: [NMusers] Problem with estimating sigma when using M3 > > method > > > > [You don't often get email from [email protected]. Learn why this is > > important at https://aka.ms/LearnAboutSenderIdentification ] > > > > Hi Hiba, > > > > I agree that often the issues should be found in the data rather than the > > model. I recommend checking the data with NMcheckData from the R package called > > NMdata. It scans for a long list of potential issues, some that will make > > Nonmem fail, some that won't. If it finds issues, they will be returned in a > > data.frame with reference to row numbers and ID's so you can easily identify > > the root cause. If you look at ?NMcheckData you may identify arguments you can > > specify to add to the list of checks the function can run. > > > > Having said this, a data/model issue can also be that your data poorly supports > > estimation of parts of your model (practical identifiability). > > NMcheckData won't help you identify such issues. > > > > NMdata: > > https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fphil > > ipdelff.github.io%2FNMdata%2F&data=05%7C01%7CHiba.Sliem%40pharmalex.co > > m%7Ca65c013926fc4c8b3b9c08db60b3fcdd%7Cff9ac3ce3c4141c3b556e1b32a662fe > > d%7C0%7C0%7C638210098390223373%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjA > > wMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sd > > ata=wjIRuNgQJ9jEvmHCKVJx94bH0IEEY9hwVoUXVf2PSjA%3D&reserved=0 > > NMcheckData manual: > > https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fphil > > ipdelff.github.io%2FNMdata%2Freference%2FNMcheckData.html&data=05%7C01 > > %7CHiba.Sliem%40pharmalex.com%7Ca65c013926fc4c8b3b9c08db60b3fcdd%7Cff9 > > ac3ce3c4141c3b556e1b32a662fed%7C0%7C0%7C638210098390223373%7CUnknown%7 > > CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXV > > CI6Mn0%3D%7C3000%7C%7C%7C&sdata=uUA1xwU0xQw6XVhuOZD0C59TmvpbutubpINW7s > > TwGjU%3D&reserved=0 > > > > An example with a few arguments that activate additional checks: > > res.checks <- > > NMcheckData(mydata,covs="WEIGHTBL",cols.num="WEIGHT",col.usubjid="USUB > > JID") Here, NMcheckData will (in addition to a bunch of other checks) > > see if WEIGHTBL exists numeric, non-na and unique within subjects, WEIGHT > > exists and is numeric and non-NA, and that ID is unique against USUBJID and > > vice versa. (Obviously, Nonmem can't read USUBJID if it contains characters, > > but you could still keep it to the right in the dataset for reference). See the > > manual above for more options. > > > > Best, > > Philip > > > > On 2023-05-26 11:06 AM, Leonid Gibiansky wrote: > > > > > Yes, SIGMA should be fixed to 1 (do not try anything else, it has to > > > be done correctly in the code first, and then we should worry about > > > how to make it work) > > > > > > For combined error, expression is > > > W = SQRT(W1**2 + W2**2) (squares in both terms) > > > > > > Do not worry about error 134, this is harmless, and you can fix it > > > any time after you get your model right. Add UNCONDITIONAL MATRIX=S > > > to the $COV step. > > > > > > For PARAMETER ESTIMATE IS NEAR ITS BOUNDARY try to add > > > NOSIGMABOUNDTEST NOOMEGABOUNDTEST NOTHETABOUNDTEST to $est record > > > > > > Most of the time, numerical difficulties come from the problems with > > > the data, so it makes sense to clean the data set first as much as > > > possible. > > > > > > Leonid > > > > > > On 5/26/2023 9:30 AM, Hiba Sliem wrote: > > > > > > > Hi > > > > > > > > I already tried fixing the value of sigma to 1, the covariance step > > > > isn't implemented when I do that. > > > > If I try fixing it to 0.144 the minimization isn't successful. > > > > > > > > I also tried a combined error model like this: > > > > LOQ=0.1 > > > > IPRED = F > > > > W1 = THETA(8)*IPRED > > > > W2 = THETA(9) > > > > W = SQRT(W1**1 + W2**2) > > > > DEL = 0 > > > > IF(W.EQ.0) DEL = 1 > > > > IRES = DV - IPRED > > > > IWRES = IRES/(W + DEL) > > > > DUM = (LOQ -IPRED)/(W + DEL) > > > > CUMD = PHI(DUM) > > > > IF(BLQ.EQ.0) THEN > > > > F_FLAG=0 > > > > Y= IPRED +W*ERR(1) > > > > ELSE > > > > F_FLAG=1 > > > > Y=CUMD > > > > MDVRES = 1 > > > > ENDIF > > > > > > > > In which case I get a PARAMETER ESTIMATE IS NEAR ITS BOUNDARY error > > > > message When trying to fix Sigma in the combined model I have a > > > > MINIMIZATION TERMINATED > > > > DUE TO ROUNDING ERRORS (ERROR=134) message. > > > > > > > > My dataset has a lot of predose samples and washouts between > > > > different periods, is it possible the issue comes from my dataset? > > > > > > > > Regards > > > > > > > > -----Original Message----- > > > > From: Leonid Gibiansky <[email protected]> > > > > Sent: Friday, 26 May 2023 14:51 > > > > To: Hiba Sliem <[email protected]>; [email protected] > > > > Subject: Re: [NMusers] Problem with estimating sigma when using M3 > > > > method > > > > > > > > [You don't often get email from [email protected]. Learn why > > > > this is important at https://aka.ms/LearnAboutSenderIdentification ] > > > > > > > > you should fix > > > > > > > > $SIGMA > > > > 1 FIX > > > > > > > > as you are already estimating the SD using THETA(8). > > > > > > > > Leonid > > > > > > > > On 5/26/2023 4:57 AM, Hiba Sliem wrote: > > > > > > > > > Hello > > > > > > > > > > I'm fairly new to nonmem, I'm currently trying to model a phase 1 > > > > > study with BLQ values, while the run was successful with no error > > > > > message, my > > > > > residual error has a %rse >70 and a confidence interval that > > > > > includes > > > > > zero. > > > > > > > > > > Here's my code: > > > > > > > > > > $ERROR > > > > > > > > > > LOQ=0.1 > > > > > > > > > > IPRED = F > > > > > > > > > > SD = THETA(8)*IPRED > > > > > > > > > > DEL = 0 > > > > > > > > > > IF(SD.EQ.0) DEL = 1 > > > > > > > > > > IRES = DV - IPRED > > > > > > > > > > IWRES = IRES / (SD + DEL) > > > > > > > > > > DUM = (LOQ -IPRED) / (SD + DEL) > > > > > > > > > > CUMD = PHI(DUM) + DEL > > > > > > > > > > IF(BLQ.EQ.0) THEN > > > > > > > > > > F_FLAG=0 > > > > > > > > > > Y= IPRED +SD*ERR(1) > > > > > > > > > > ELSE > > > > > > > > > > F_FLAG=1 > > > > > > > > > > Y=CUMD > > > > > > > > > > MDVRES = 1 > > > > > > > > > > ENDIF > > > > > > > > > > $EST METHOD=1 INTERACTION LAPLACIAN PRINT=5 MAX=9999 SIG=3 SLOW > > > > > NUMERICAL MSFO=*.msf > > > > > > > > > > $SIGMA > > > > > > > > > > 0.38 ;[P] sigma(1,1) (estimated in a previous model) > > > > > > > > > > Furthermore, when trying to fit this model to my phase 2 dataset, > > > > > covariance step fails when I implement it. > > > > > > > > > > Any suggestions are welcome > > > > > > > > > > Thank you