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 <phi...@delff.dk>
Sent: Friday, 26 May 2023 21:42
To: Leonid Gibiansky <lgibian...@quantpharm.com>
Cc: Hiba Sliem <hiba.sl...@pharmalex.com>; nmusers <nmusers@globomaxnm.com>
Subject: Re: [NMusers] Problem with estimating sigma when using M3 method
[You don't often get email from phi...@delff.dk. 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://philipdelff.github.io/NMdata/
NMcheckData manual:
https://philipdelff.github.io/NMdata/reference/NMcheckData.html
An example with a few arguments that activate additional checks:
res.checks <-
NMcheckData(mydata,covs="WEIGHTBL",cols.num="WEIGHT",col.usubjid="USUBJID")
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 <lgibian...@quantpharm.com>
>> Sent: Friday, 26 May 2023 14:51
>> To: Hiba Sliem <hiba.sl...@pharmalex.com>; nmusers@globomaxnm.com
>> Subject: Re: [NMusers] Problem with estimating sigma when using M3
>> method
>>
>> [You don't often get email from lgibian...@quantpharm.com. 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
>>>
