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From: Yassine Kamal Lyauk <yassinekamallyauk_at_gmail.com>

Date: Thu, 10 Dec 2015 10:10:21 +0100

Dear Åsa,

Thank you very much for your suggestions.

I have tried both approaches and the one you prefer (using CALLSIMETA) is

more intuitive I also think. It seems to work fine.

In the code you suggest (using CALL RANDOM) a 70 kg weight for all subjects

is attained. I think this is due to R = 0 - when I remove it, I start

getting different weight values that are within the truncation limits.

Lastly, I am very interested in hearing more regarding your end comment on

correlation of the simulated covariates. Could you briefly specify what you

mean/how to implement this (i.e. how to preserve the same correlation

patern as in the original data set). I am simulating distributions for both

continuous (e.g. weight, age) and categorical (e.g. SEX, SNPs) covariates.

It is for the categorical covariates that I use separate CALL RANDOM blocks

while drawing from UNIFORM distributions.

Best regards,

Yassine

2015-12-08 14:22 GMT+01:00 Johansson, Åsa <asa.johansson_at_astrazeneca.c=

om>:

*> Dear Yassine,
*

*>
*

*>
*

*>
*

*> I prefer the second option, where you simulate new ETAs until they fulfil=
*

l

*> your requirements. THETA(1) is the mean value of WT while ETA(1) is a
*

*> random variable with variance OMEGA. You should just add the known mean
*

*> value of WT (e.g. 70) as the initial estimate of THETA(1) and the known
*

*> variance of weight (stdev^2; e.g. 100) as the initial estimate of OMEGA f=
*

or

*> ETA(1). The simulations will always be based on your initial estimates so
*

*> there is no need to specify the actual values (i.e. mean 70 and standard
*

*> deviation 10) directly in the code. By the way, I think you might need to
*

*> add a .AND.NEWIND.NE.2 to the IF-statement (IF(ICALL.EQ.4.AND.NEWIND.NE.2=
*

).

*>
*

*>
*

*>
*

*> If you anyhow want to use the first option (simulate random numbers from =
*

a

*> normal distribution), the CALL RANDOM(5, R) needs to be specified in the
*

*> DOWHILE-loop. You want to simulate a new random variable if the first one
*

*> doesn’t fulfill your requirements. This code could look something=
*

like this:

*>
*

*>
*

*>
*

*> IF(ICALL.EQ.4.AND.NEWIND.NE.2) THEN
*

*>
*

*> R = 0
*

*>
*

*> WT =70+10*R
*

*>
*

*> DOWHILE (WT.LT.50.OR.WT.GT.100)
*

*>
*

*> CALL RANDOM(5,R)
*

*>
*

*> ENDDO
*

*>
*

*> ENDIF
*

*>
*

*> IF(ICALL.EQ.4) WT=70+10*R
*

*>
*

*>
*

*>
*

*> If the simulated weight is outside the specified limits (i.e. less than 5=
*

0

*> or greater than 100), a new random value will be simulated until the weig=
*

ht

*> is between these limits. I have not tried the code myself but I’m=
*

pretty

*> sure it should work.
*

*>
*

*>
*

*>
*

*> You write in your question that you have 4 different covariates which you
*

*> want to simulate. Be aware that the simulated covariates need to be
*

*> correlated in the same way as your original data to give valid results.
*

*>
*

*>
*

*>
*

*> Kind regards,
*

*>
*

*> Åsa
*

*>
*

*>
*

*>
*

*>
*

*>
*

*> *From:* owner-nmusers_at_globomaxnm.com [mailto:owner-nmusers_at_globomaxnm.com=
*

]

*> *On Behalf Of *Yassine Kamal Lyauk
*

*> *Sent:* den 8 december 2015 13:14
*

*> *To:* nmusers_at_globomaxnm.com
*

*> *Subject:* [NMusers] Truncated normal distribution for continuous
*

*> covariate with known mean and standard error
*

*>
*

*>
*

*>
*

*> Dear NMUsers,
*

*>
*

*>
*

*>
*

*> I would like to implement a truncated normal distribution for weight for
*

*> use in a simulation, with a pre-specified mean and standard deviation.
*

*>
*

*>
*

*>
*

*> What works so far is this (w.o. truncation, which I found in the NONMEM
*

*> user guides):
*

*>
*

*>
*

*>
*

*> $PK
*

*>
*

*> IF (ICALL.EQ.4.AND.NEWIND.NE.2) THEN
*

*>
*

*> CALL RANDOM(5,R)
*

*>
*

*> ENDIF
*

*>
*

*> IF (ICALL.EQ.4) WT=70+10*R; mean of 70 with SD 10
*

*>
*

*>
*

*>
*

*> $SIM (12345) (...) (...) (...) (54676 NORMAL); I have a total of five
*

*> random number generators i.e. 4 different covariates, which I want to
*

*> simulate distributions for.
*

*>
*

*>
*

*>
*

*> Adding a DOWHILE loop does not seem to work (either I get error messages
*

*> or NONMEM stagnates after performing the sims and never finishes) - I thi=
*

nk

*> it's a coding issue on my part, in regard to the IF THEN ELSE statements
*

*> and DOWHILE statement:
*

*>
*

*>
*

*>
*

*> IF (ICALL.EQ.4.AND.NEWIND.NE.2) THEN
*

*>
*

*> CALL RANDOM(5,R)
*

*>
*

*> ENDIF
*

*>
*

*> IF (ICALL.EQ.4) WT=70+10*R;
*

*>
*

*> DOWHILE (WT.GT.50.OR.WT.LT.100); truncation limits of 50 and 100
*

*>
*

*> ENDDO
*

*>
*

*>
*

*>
*

*> Other ways to generate a normal truncated distribution can be coded as
*

*> (from Metrum Institute course 210):
*

*>
*

*>
*

*>
*

*> $PK
*

*>
*

*> IF (ICALL.EQ.4) THEN
*

*>
*

*> WT=THETA(1) + ETA(1)
*

*>
*

*> DOWHILE (WT.LT.20.OR.WT.GT.100)
*

*>
*

*> CALL SIMETA(ETA)
*

*>
*

*> WT=THETA(1) + ETA(1)
*

*>
*

*> ENDDO
*

*>
*

*> ENDIF
*

*>
*

*>
*

*>
*

*> $SIM (2345 NEW)
*

*>
*

*>
*

*>
*

*> Yet, I am unsure of how to incorporate my known mean and standard
*

*> deviation using this approach.
*

*>
*

*>
*

*>
*

*> Hope you can help. Thanks in advance.
*

*>
*

*>
*

*>
*

*> Best regards,
*

*>
*

*>
*

*>
*

*> Yassine
*

*>
*

*>
*

*>
*

*>
*

*>
*

*>
*

*>
*

*>
*

*>
*

*>
*

*>
*

*>
*

*>
*

*> ------------------------------
*

*>
*

*> *Confidentiality Notice: *This message is private and may contain
*

*> confidential and proprietary information. If you have received this messa=
*

ge

*> in error, please notify us and remove it from your system and note that y=
*

ou

*> must not copy, distribute or take any action in reliance on it. Any
*

*> unauthorized use or disclosure of the contents of this message is not
*

*> permitted and may be unlawful.
*

*>
*

*>
*

--

Best regards / Med venlig hilsen

Yassine Kamal Lyauk

Received on Thu Dec 10 2015 - 04:10:21 EST

Date: Thu, 10 Dec 2015 10:10:21 +0100

Dear Åsa,

Thank you very much for your suggestions.

I have tried both approaches and the one you prefer (using CALLSIMETA) is

more intuitive I also think. It seems to work fine.

In the code you suggest (using CALL RANDOM) a 70 kg weight for all subjects

is attained. I think this is due to R = 0 - when I remove it, I start

getting different weight values that are within the truncation limits.

Lastly, I am very interested in hearing more regarding your end comment on

correlation of the simulated covariates. Could you briefly specify what you

mean/how to implement this (i.e. how to preserve the same correlation

patern as in the original data set). I am simulating distributions for both

continuous (e.g. weight, age) and categorical (e.g. SEX, SNPs) covariates.

It is for the categorical covariates that I use separate CALL RANDOM blocks

while drawing from UNIFORM distributions.

Best regards,

Yassine

2015-12-08 14:22 GMT+01:00 Johansson, Åsa <asa.johansson_at_astrazeneca.c=

om>:

l

or

).

a

like this:

0

ht

pretty

]

nk

ge

ou

--

Best regards / Med venlig hilsen

Yassine Kamal Lyauk

Received on Thu Dec 10 2015 - 04:10:21 EST

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