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Re: [External] Re: Error in Power-model for covariate selection

From: Jakob Ribbing <jakob.ribbing>
Date: Wed, 20 Nov 2019 07:29:26 +0100

Hi Sumeet,

PsN does not change in the dataset so that -99 becomes the median value, =
it is handled in the code (the nonmem control stream).
This is why you need to check the file “covariate_statistics.txt=
to see that PsN understood these were missing values.
For example, reading in a datafile in R, the option na.strings=“=
-99” would not understand -99.0 or scientific notation as NA.


However, you can also leave scm aside for a while and just manually run =
the problematic model with BMI on CL*.
Add the below lines to your base model (or locate the problematic run =
created by scm, and work with that).

Not related to this, but very important, I think you need to add INTER =
on the $EST line, as you have a proportional error model:
F*ERR(1) means you have an eta-eps interaction in your model), it would =
not have been needed for additive error on the log-transformed scale.
However, apparently with modern nonmem it is fine to always use FOCE =
INTER, even if not needed (i.e. even if there is no eta-eps =
interaction)- it should not be slower or less stable.


Start with some code that will make nonmem exit if there are negative =
BMI values - without this line nonmem would run the power model for BMI =
as intended,
But the purpose is to find out why it is not working in scm.
For example, it may be that you have some BMI values that are “.=
, instead of numeric values. Nonmem will interpret these as zero. =
Not sure what PsN would do...

Best wishes

Jakob


Add these lines:

IF(BMI.LE.0) EXIT 11

TVCL = THETA(2)
IF(BMI.GT.0) TVCL = (BMI/25)**THETA(5)
CL = TVCL*EXP(ETA(2)) ; Clearance L/h


; Initial estimates
THETA 0.001; BMI.on.CL; 5.




*As a side note, one would not normally test all covariates on all =
parameters (using multiple equations). But let’s focus at the =
technical question in this thread.


> On 20 Nov 2019, at 06:23, Singla, Sumeet K <sumeet-singla
wrote:
>
> Jakob,
>
> Yes, in the pooled dataset from different studies, one dataset =
didn’t have BMI values. Initially, I used =
“missing_values_token” option in the PsN command line =
and by default it is -99. However, power model still gave errors. Now I =
just checked the dataset in model run directory and it shows that =
despite using that option, PsN never replaced missing values with -99. =
Maybe, I should manually enter it and run it again. Also, because its =
negative value, I thought it doesnt make sense, and instead I provided =
median BMI in place of missing values. Didn’t work that way =
either.
>
> Regards,
> Sumeet Singla
>
>
>
>> On Nov 19, 2019, at 11:10 PM, Jakob Ribbing =
<jakob.ribbing
>>
>>  Hi Sumeet,
>>
>> It is great that you have considered already that covarite values do =
not include zero or negative values, as that would not work with the =
power model.
>> Did you have any missing values, and how were they coded?
>>
>> I would recommend to code these (in your data file) using the default =
-99 in your datafile.
>> You can check the PsN file “covariate_statistics.txt” =
for your scm run: For BMI in particular, were there missing values =
detected by PsN and is the minimum value >0 or is it -99?
>>
>> Best regards
>>
>> Jakob
>>
>>> On 20 Nov 2019, at 05:42, Singla, Sumeet K <sumeet-singla
<mailto:sumeet-singla
>>>
>>> Hello Everyone,
>>>
>>> I am using PsN enabled SCM option in Pirana to analyze selection of =
covariates in the PK 2- Comp model. As long as I am using just linear =
and exponential covariate model, everything runs fine. However, as soon =
as I add power-model in the mix, “some” power models =
involving continuous covariates on parameters fail to run and it gives =
me the following error:
>>> “HESSIAN OF POSTERIOR DENSITY IS NON-POSITIVE-DEFINITE =
DURING SEARCH”
>>> I understand that individual PK parameter search might be moving =
into 0 or negative territory. I am trying to fix it but didn’t =
have much success. I have set lower bounds, removed lower bounds, =
changed order of model in valid states option in scm configuration file, =
dataset doesn’t contain any 0 or negative value, but nothing is =
working. FYI: I don’t need to test hockey-stick relation as =
literature and data doesn’t support it, power model can only be =
used on continuous covariates and I have turned on parallel states =
option.
>>> This is how part of my scm results, followed by scm configuration =
file, followed by base model for PK 2-Comp looks like:
>>>
>>> MODEL TEST BASE OFV NEW OFV TEST OFV =
(DROP) GOAL dDF SIGNIFICANT PVAL
>>> CLAGE-2 PVAL 2618.02603 2616.67228 1.35375 =
> 3.84150 1 0.244620
>>> CLAGE-5 PVAL 2618.02603 2616.36273 1.66330 =
> 3.84150 1 0.197160
>>> CLAGE-4 PVAL 2618.02603 2616.65232 1.37371 =
> 3.84150 1 0.241180
>>> CLBMI-2 PVAL 2618.02603 2612.96657 5.05946 =
> 3.84150 1 YES! 0.024492
>>> CLBMI-5 PVAL 2618.02603 FAILED FAILED =
> 3.84150 1 999
>>>
>>> SCM FILE:
>>> “
>>> search_direction=both
>>> p_forward=0.05
>>> p_backward=0.01
>>> continuous_covariates=BMI,AGE
>>> categorical_covariates=USER,SEX
>>> parallel_states=1
>>> retries=2
>>> threads=6
>>> tweak_inits=1
>>> ;;1-NotIncluded, 2-LINEAR, 3-Hockey Stick Relation, 4-Exponential, =
5-Power
>>> [test_relations]
>>> CL=AGE,SEX,BMI,USER
>>> V1=AGE,SEX,BMI,USER
>>> V2=AGE,SEX,BMI,USER
>>> Q=AGE,SEX,BMI,USER
>>> [valid_states]
>>> continuous = 1,2,5,4
>>> categorical = 1,2
>>> “
>>> NONMEM Control Stream:
>>>
>>> $SUBROUTINE ADVAN3 TRANS4
>>>
>>> $PK
>>>
>>> TVV1 = THETA(1) ;Central Volume of =
distribution in L
>>> V1 = TVV1*EXP(ETA(1))
>>> TVCL = THETA(2)
>>> CL = TVCL*EXP(ETA(2)) ; Clearance L/h
>>> TVQ = THETA(3)
>>> Q = TVQ*EXP(ETA(3)) ;Intercompartment Clearance
>>> TVV2 = THETA(4)
>>> V2 = TVV2*EXP(ETA(4)) ;Peripheral volume in L
>>> S1=V1
>>>
>>> $ERROR
>>> IPRED=F
>>> Y= F + F*ERR(1); Proportional Error
>>>
>>> $THETA
>>> (0, 16); [V1] based on PK 2 Comp
>>> (0, 255); [CL] based on PK 2 Comp
>>> (0, 33.5); [Q] based on PK 2 Comp
>>> (0, 29.7); [V2] based on PK 2 Comp
>>>
>>> $OMEGA
>>> (0, 0.08); [P] omega(1,1)
>>> (0, 0.159); [P] omega(2,2)
>>> (0, 0.140); [P] omega(3,3)
>>> (0, 0.19); [P] omega(4,4)
>>>
>>> $SIGMA
>>> (0, 0.06) ;sigma1
>>>
>>> $EST METHOD=1 PRINT=5 MAXEVAL=9999 SIG=3 NOABORT
>>>
>>>
>>> Regards,
>>>
>>> Sumeet K. Singla
>>> Ph.D. Candidate
>>> Division of Pharmaceutics and Translational Therapeutics
>>> College of Pharmacy | University of Iowa
>>> Iowa City, Iowa
>>> sumeet-singla
>>> 518.577.5881
>>


Received on Wed Nov 20 2019 - 01:29:26 EST

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