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NONMEM vs. R for linear mixed-effects

From: Dennis Fisher <fisher>
Date: Wed, 28 Feb 2018 08:16:45 -0800


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) =

The critical code is:

                SLOPE = THETA(2) + ETA(2)
                Y = INTERCEPT + SLOPE * TIME + =

R: LMER package:
        lmer(DV ~ TIME + (1|PERIOD), data=DATA, REML=FALSE)
        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 Fisher MD
P < (The "P Less Than" Company)
Phone / Fax: 1-866-PLessThan (1-866-753-7784) <>

Received on Wed Feb 28 2018 - 11:16:45 EST

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