Please make sure to read this at
The NMsim website
where you
can browse several vignettes with examples on specific topics.
NMsim
is an R package that can simulate Nonmem models (using the
NMsim
function) based on just a simulation data set and a path to an
estimation control stream. It will also retrive and combine output
tables with input data once Nonmem has finished and return the results
to R.
The interface is “seamless” or fully integrated in R. Run a simulation
of the (estimated) model stored in “path/to/file.mod” using the
simulation input data set stored in the variable data.sim
this way:
simres <- NMsim(file.mod="/path/to/file.mod",
data=data.sim)
You will quickly learn to do this on your own models, but if you can’t wait to see this working, you can do the following:
data.sim <- read.csv(system.file("examples/derived/dat_sim1.csv",package="NMsim"))
simres <- NMsim(file.mod=system.file("examples/nonmem/xgxr021.mod",package="NMsim"),
data=data.sim,
dir.sims=".")
where dir.sims
may be needed because the model in this case may be in
a read-only location.
Notice, that could be any working Nonmem model as long as the provided simulation data set is sufficient to run it. We are ready to plot:
library(ggplot2)
datl <- as.data.table(simres) |>
melt(measure.vars=cc(PRED,IPRED,Y))
ggplot(datl,aes(TIME,value,colour=variable))+
geom_line(data=function(x)x[variable!="Y"])+
geom_point(data=function(x)x[variable=="Y"])+
labs(x="Hours since first dose",y="Concentration (ng/mL)")
This example was a simulation of a multiple dose regimen with a loading
dose using a model estimated on single dose data. It is from the first
vignette
NMsim-basics.html
.
NMsim
has a flexible way to define simulation methods. The following
methods are currently provided:
method.sim=NMsim_default
)method.sim=NMsim_typical
)method.sim=NMsim_known
)method.sim=NMsim_VarCov
)method.sim=NMsim_asis
)To learn how to run these simulations on your Nonmem models, get started
with
NMsim-basics.html
.
It is really easy.
In addition, NMsim
can simulate multiple models at a time. E.g., if a
bootstrap run of a model is available, NMsim can run the simulation with
each of the bootstrap models and collect all the results in one dataset.
This provides a robust and easy way to simulate a Nonmem model with
uncertainty.
You can also write your own methods, if you have some other Nonmem-based
simulation (or other job) you want to automate using NMsim
.
Many features are available. Prominent ones are:
SUBPROBLEMS
feature avaible
through the subproblems
argumenttransform
argument.If residual variability is not implemented in the simulated model,
NMsim
provides a way (addResVar()
) to add residual variability in R
after the simulation has been run.
One strength of NMsim
is that it does not simulate, translate or
otherwise interpret a Nonmem model. Instead, it automates the Nonmem
simulation workflow (including execution of Nonmem) and wraps it all
into one R function. In the example given above, NMsim
will do the
following:
file.mod
($INPUT
and $DATA matching the saved simulation data set; $SIMULATE instead
of $ESTIMATION and $COVARIANCE)file.ext
)This eliminates the need for re-implementation of a model for simulation
purposes. On the other hand, this also means that NMsim
can’t work
without Nonmem.
NMsim
can call Nonmem directly or via PSN
. If NMsim
is run on a
system where Nonmem cannot be executed, NMsim
can still prepare the
simulation control stream and datafile.
NMsim
is in itself a relatively small R package. It makes extensive
use of functionality to handle Nonmem data and control streams provided
by the R package NMdata
.
The methods currently provided by NMsim
will work with (many or most)
Pop PK models and most continuous-scale PD models. Methods are currently
not provided for for time-to-event models. Also, depending on the coding
of the models, other censored data models may not work out of the box,
because the model may not have a single variable (in Nonmem) that
simulates the wanted information for all data rows, as their
interpretation may depend on other values.
The input data set must contain whatever variables are needed by the
Nonmem model. A common issue is if the Nonmem model uses a covariate
that is not in the simulation input data set. NMdata
’s
NMcheckData
is a good help identifying input data issues before running Nonmem - and
when Nonmem acts unexpectedly.
Nonmem may not be the fastest simulator out there. But actually most
often, the reason Nonmem is slow at providing a simulation result is
that it takes a long time writing the $TABLE
files (yes, that can
account for 90% or more of the time Nonmem spends). NMsim
provides a
simple way to get around this. The argument text.table
can be used to
define only the columns needed in the simulation output (which may be as
little as PRED
, IPRED
, and a couple more - remember the input data
is merged back automatically). As a result, NMsim
may still be slower
than a re-implementation in a different framework. But it’s extremely
easy to do.
NMsim is dependent on running Nonmem. Often, that will mean Nonmem must
be available on the same system as the one running R. However, if Nonmem
is run on a separate system through qsub
or in another way initiates
Nonmem on another system, that will work too. Then however, only if R
can read the file system where Nonmem writes the results, it can
retrieve the results.
NMsim does not need PSN but can use it. However, not all features are
available with PSN, so for some features you will have to specify the
path to the Nonmem executable (say path.nonmem=/path/to/nmfe75
or any
Nonmem executable you want to use). Specifically of the simulation types
currently available, simulation of known subjects is not possible using
PSN (but works if a Nonmem executable is provided).
If PSN is used, NMsim
uses PSN’s execute
to run models. In addition,
NMsim
by default uses PSN’s update_inits
to update initial values in
control streams, if PSN is available. NMsim
does also include its own
simple function to do this if PSN
is not available.
NMsim
reliable?Importantly, NMsim
does not (at least not by default) modify,
translate or simulate the model itself. It does modify control stream
sections $INPUT
, $DATA
, $ESTIMATION
, $SIMULATION
, $THETA
,
$OMEGA
, $SIGMA
, $TABLE
as needed. The fact that NMsim
allows for
skipping the re-implementation but just uses Nonmem to simulate the
Nonmem model as is, eliminates the risk of discrepancies between the
estimated model and the simulated model.
The produced control stream is saved together with simulation data set
open for manual inspection and can obviously be run with Nonmem
independently of NMsim
.
NMsim
includes functions (NMcreateDoses
and addEVID2
) to very
easily create simulation data sets. While one certainly does not need to
use these functions to use NMsim
, they do add to the package providing
a framework that enables a complete simulation workflow in only 5-15
simple lines of R code.
There are several other packages out there that can do this, and NMsim
may not be your best choice if this feature is all you are looking for.
However, running Nonmem using the NMexec()
function provided by
NMsim
has one important advantage in that it saves the input data
together with the Nonmem control streams. This ensures that output data
can be merged with input data as it went into the model, even if the
input data file should be modified or lost.
NMexec
will submit model runs to a cluster by default. This can be
switched off for running Nonmem locally. Please notice the jobs are
submitted to a cluster in a very specific way using PSN
. If your setup
is different, this is for now not supported. Please use
NMexec(sge=FALSE)
in that case (which may not be desirable). Notice
that simulations are not done on a cluster by default so you may still
be able to use NMsim
.
NMsim
is on CRAN, MPN and github:
## From CRAN/MPN repositories
install.packages("NMsim")
## From github
library(remotes)
install_github("philipdelff/NMsim")