From: Dennis Fisher <*fisher*>

Date: Wed, 28 Feb 2018 09:27:00 -0800

Leonid

I omitted one critical piece of information:

$INPUT PERIOD=ID

I think that your approach implies an order to the data from different =

periods — whereas making PERIOD=ID allows NONMEM to see each =

treatment as a separate subject (i.e., the random effect)

Dennis

Dennis Fisher MD

P < (The "P Less Than" Company)

Phone / Fax: 1-866-PLessThan (1-866-753-7784)

www.PLessThan.com <http://www.plessthan.com/>

*> On Feb 28, 2018, at 9:23 AM, Leonid Gibiansky =
*

<lgibiansky

*>
*

*> Just checking: you have treatment effect in R but no effect in Nonmem, =
*

am I correct? (I have not used R nlme for a long time, may be I am =

interpreting it incorrectly)

*>
*

*> Should you code nonmem as
*

*> $PRED
*

*> INTERCEPT = THETA(1) + THETA(3)*PERIOD + ETA(1)
*

*> SLOPE = THETA(2) + THETA(4)*PERIOD + ETA(2)
*

*> Y = INTERCEPT + SLOPE * TIME + EPS(1)
*

*>
*

*> Leonid
*

*>
*

*> On 2/28/2018 11:16 AM, Dennis Fisher wrote:
*

*>> Colleagues
*

*>> I am implementing a linear mixed-effects model in R.
*

*>> Out of curiosity (and to confirm that I was doing the right thing), I =
*

wrote the code initially in NONMEM, then tried to replicate the results =

in R.

*>> The dataset is four (identical) treatments for one subject and the =
*

data are reasonably linear.

*>> For most subjects, the results from the NONMEM analysis are nearly =
*

identical to those from R.

*>> But, for one subject, the SLOPE/INTERCEPT are sufficiently different =
*

to concern me that I am implementing one of these (or possibly both) =

incorrectly.

*>> The critical code is:
*

*>> *NONMEM:*
*

*>> $PREDINTERCEPT= THETA(1) + ETA(1)
*

*>> SLOPE= THETA(2) + ETA(2)
*

*>> Y= INTERCEPT + SLOPE * TIME + EPS(1)
*

*>> *R: LMER package:*
*

*>> lmer(DV ~ TIME + (1|PERIOD), data=DATA, REML=FALSE)
*

*>> where:
*

*>> DV is the dependent variable
*

*>> PERIOD distinguishes the treatments (and is a factor)
*

*>> *R: NLME package:*
*

*>> lme(DV ~ TIME, random = ~ 1|PERIOD, data=DATA, method="ML")
*

*>> The two R packages yield identical results.
*

*>> Graphics from NONMEM and R differ slightly but there is no obvious =
*

preference between these approaches.

*>> Any thoughts as to a possible explanation?
*

*>> Dennis
*

*>> Dennis Fisher MD
*

*>> P < (The "P Less Than" Company)
*

*>> Phone / Fax: 1-866-PLessThan (1-866-753-7784)
*

*>> www.PLessThan.com <http://www.plessthan.com/> =
*

<http://www.plessthan.com/ <http://www.plessthan.com/>>

Received on Wed Feb 28 2018 - 12:27:00 EST

Date: Wed, 28 Feb 2018 09:27:00 -0800

Leonid

I omitted one critical piece of information:

$INPUT PERIOD=ID

I think that your approach implies an order to the data from different =

periods — whereas making PERIOD=ID allows NONMEM to see each =

treatment as a separate subject (i.e., the random effect)

Dennis

Dennis Fisher MD

P < (The "P Less Than" Company)

Phone / Fax: 1-866-PLessThan (1-866-753-7784)

www.PLessThan.com <http://www.plessthan.com/>

<lgibiansky

am I correct? (I have not used R nlme for a long time, may be I am =

interpreting it incorrectly)

wrote the code initially in NONMEM, then tried to replicate the results =

in R.

data are reasonably linear.

identical to those from R.

to concern me that I am implementing one of these (or possibly both) =

incorrectly.

preference between these approaches.

<http://www.plessthan.com/ <http://www.plessthan.com/>>

Received on Wed Feb 28 2018 - 12:27:00 EST