From: hai le ba <*lebahai01*>

Date: Thu, 5 Nov 2020 11:15:17 +0100

Dear Sir Jakob and Jeroen,

Thanks for your reply.

In fact, i've a one compartment model, with two levels of random effect

(IIV, EPS) and covariate effect on CL, V.

$PK

MU_1= LOG(THETA(1))

KA=EXP(MU_1+ETA(1))

MU_2=LOG(THETA(2)+ THETA(4)*LOG(AGE/60.13))

CL= EXP(MU_2+ETA(2))

MU_3=LOG(THETA(3)+THETA(5)*LOG(WEI/66.49))

V=EXP(MU_3+ETA(3))

S2=V

IF (TIME.LT.12) TAD=TIME

IF (TIME.GE.12.AND.TIME.LT.24) TAD=TIME-12

IF (TIME.GE.12.AND.TIME.LT.36) TAD=TIME-24

IF (TIME.GE.36.AND.TIME.LT.48) TAD=TIME-36

$ERROR

EP1=EPS(1)

IPRED=F

IRES=DV-IPRED

W=F

IWRES=IRES/W

Y=IPRED+EPS(1)*W

I've used this model to simulate the complete database (10 000 IDs). From

this database, the smaller dataset was randomly generated with respect to

the rule of one PK sample per each ID. After that, I want to check how

many IDs are necessary to get a good estimation for each method ( FOCEI,

SAEM, BAYES, composite (ITS/SAEM/IMP/BAYES))?

For FOCEI, I can't get convergence.

For SAEM, I've got some troubles with the stability for EPS when I tried to

change the SEED status?

Otherwise, the good estimations were obtained with RSE (10-20%) for the

other parameters.

convergence for this setting of NBURN.

"

Convergence achieved

STOCHASTIC PORTION WAS COMPLETED

REDUCED STOCHASTIC PORTION WAS COMPLETED"

Could you give me some instructions?

Many thanks in advance for your responses.

Best regards,

Hai, LE Ba

Ph.D Student

La faculté de Pharmacie - Aix Marseille université

Hanoi university of Pharmacy

Email: lebahai01

hailb

bahai.le

On Thu, Nov 5, 2020 at 10:43 AM Jakob Ribbing <jakob.ribbing

m>

wrote:

*> Dear LE Ba,
*

*>
*

*> You did not share much information on your model, except that you have on=
*

e

*> observations per subject and that EPS (or rather sigma) is estimated (I
*

*> assume then a continuous endpoint).
*

*> Is thisyour only level of random effect, so that you have no omega/eta fo=
*

r

*> IOV or IIV? With one observations per subject it will be difficult to fin=
*

d

*> support for more than one level.
*

*> And for a model with only residual error (no IIV), would not the FO
*

*> Estimation method be sufficient, so why would you need to use SAEM for th=
*

is

*> data/model?
*

*>
*

*> Best regards
*

*>
*

*> Jakob
*

*>
*

*>
*

*>
*

*> Jakob Ribbing, Ph.D.
*

*>
*

*> Senior Consultant, Pharmetheus AB
*

*>
*

*>
*

*> Cell/Mobile: +46 (0)70 514 33 77
*

*>
*

*> Jakob.Ribbing *

*>
*

*> www.pharmetheus.com
*

*>
*

*> Phone, Office: +46 (0)18 513 328
*

*>
*

*> Uppsala Science Park, Dag Hammarskjölds väg 36B
*

*> SE-752 37 Uppsala, Sweden
*

*>
*

*>
*

*>
*

*> This communication is confidential and is only intended for the use of th=
*

e

*> individual or entity to which it is directed. It may contain
*

*> information that is privileged and exempt from disclosure under applicabl=
*

e

*> law. If you are not the intended recipient please notify us immediately.
*

*> Please do not copy it or disclose its contents to any other person.
*

*>
*

*>
*

*>
*

*>
*

*> On 5 Nov 2020, at 09:41, hai le ba <lebahai01 *

*>
*

*> Hello nmusers,
*

*> I've tried to test the SAEM method on my dataset (1 sample for each ID)
*

*> with different SEED status. Sometimes, I've got good results (with RSE
*

*> <50%), but sometimes I can't get it.
*

*>
*

*> Example:
*

*> $EST METHOD=SAEM INTERACTION NBURN=3000 NITER=500 AUTO=1 PRINT==
*

100

*> SEED=123456789
*

*> $EST METHOD=IMP EONLY=1 PRINT=1 NITER=5 ISAMPLE=1000 MAPITER==
*

0

*> I've got EPS : 0.00064 (RSE=63%)
*

*>
*

*> but
*

*> $EST METHOD=SAEM INTERACTION NBURN=3000 NITER=500 AUTO=1 PRINT==
*

100

*> SEED=123235489
*

*> $EST METHOD=IMP EONLY=1 PRINT=1 NITER=5 ISAMPLE=1000 MAPITER==
*

0

*> I've got EPS : 0.0223 (RSE=18%)
*

*>
*

*> Could i trust on the good results (EPS=0.0223)? How can we control this
*

*> problem?
*

*>
*

*> Thank you very much,
*

*> Hai, LE Ba
*

*> Ph.D Student
*

*> La faculté de Pharmacie - Aix Marseille université
*

*> Hanoi university of Pharmacy
*

*> Email: lebahai01 *

*> hailb *

*> bahai.le *

*>
*

*>
*

*>
*

Received on Thu Nov 05 2020 - 05:15:17 EST

Date: Thu, 5 Nov 2020 11:15:17 +0100

Dear Sir Jakob and Jeroen,

Thanks for your reply.

In fact, i've a one compartment model, with two levels of random effect

(IIV, EPS) and covariate effect on CL, V.

$PK

MU_1= LOG(THETA(1))

KA=EXP(MU_1+ETA(1))

MU_2=LOG(THETA(2)+ THETA(4)*LOG(AGE/60.13))

CL= EXP(MU_2+ETA(2))

MU_3=LOG(THETA(3)+THETA(5)*LOG(WEI/66.49))

V=EXP(MU_3+ETA(3))

S2=V

IF (TIME.LT.12) TAD=TIME

IF (TIME.GE.12.AND.TIME.LT.24) TAD=TIME-12

IF (TIME.GE.12.AND.TIME.LT.36) TAD=TIME-24

IF (TIME.GE.36.AND.TIME.LT.48) TAD=TIME-36

$ERROR

EP1=EPS(1)

IPRED=F

IRES=DV-IPRED

W=F

IWRES=IRES/W

Y=IPRED+EPS(1)*W

I've used this model to simulate the complete database (10 000 IDs). From

this database, the smaller dataset was randomly generated with respect to

the rule of one PK sample per each ID. After that, I want to check how

many IDs are necessary to get a good estimation for each method ( FOCEI,

SAEM, BAYES, composite (ITS/SAEM/IMP/BAYES))?

For FOCEI, I can't get convergence.

For SAEM, I've got some troubles with the stability for EPS when I tried to

change the SEED status?

Otherwise, the good estimations were obtained with RSE (10-20%) for the

other parameters.

convergence for this setting of NBURN.

"

Convergence achieved

STOCHASTIC PORTION WAS COMPLETED

REDUCED STOCHASTIC PORTION WAS COMPLETED"

Could you give me some instructions?

Many thanks in advance for your responses.

Best regards,

Hai, LE Ba

Ph.D Student

La faculté de Pharmacie - Aix Marseille université

Hanoi university of Pharmacy

Email: lebahai01

hailb

bahai.le

On Thu, Nov 5, 2020 at 10:43 AM Jakob Ribbing <jakob.ribbing

m>

wrote:

e

r

d

is

e

e

100

0

100

0

Received on Thu Nov 05 2020 - 05:15:17 EST