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

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.

wrote:

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.

<jakob.ribbing

not include zero or negative values, as that would not work with the =

power model.

-99 in your datafile.

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?

<mailto:sumeet-singla

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:

DURING SEARCH”

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.

file, followed by base model for PK 2-Comp looks like:

(DROP) GOAL dDF SIGNIFICANT PVAL

5-Power

distribution in L

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