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Performing Simulations

The Pmetrics simulator is a powerful Monte Carlo engine that is smoothly integrated with Pmetrics inputs and outputs. Unlike NPAG and IT2B, it is run from within R. No batch file is created or terminal window opened. However, the actual simulator is a Fortran executable compiled and run in an OS shell.

To complete a simulator run you must include data and a model object. These can be previously loaded objects with PM_data() and PM_model() or loaded on the fly by specifying file names of appropriate files in the current working directory, which you can check with getwd() and list.files(), e.g., .csv for data and .txt for models, at the time of simulation. The model dictates the equations and the data file serves as the template specifying doses, observation times, covariates, etc.

The other mandatory item is a prior probability distribution for all random parameter values in the model. This is referenced by the poppar argument, detailed below.

You run the simulator in two ways.

  • Use the $sim() method for PM_result() objects. This takes poppar from the PM_result$final field and the model from the PM_result$model field, so only a data object needs to be specified at minimum. If no data object is specified, it will use the PM_result$data as a template, i.e. the same data used to fit the model. An example is below. You don’t have to supply the model or poppar, because they are included in run1, with poppar taken from the run$final field. Supplying a different PM_data object as dat allows you to use the model and population prior from run1 with a different dosing and observation data template. If you omit dat in this example, the original data in run1 would serve as the template.
run1 <- PM_load(1)
sim1 <- run1$sim(data = dat, nsim = 100, limits = NA)
  • Use PM_sim$new(). This simulates without necessarily having completed a fitting run previously, i.e., there may be no PM_result() object. Here, you manually specify values for poppar, model, and data. These can be other Pmetrics objects, or de novo, which can be useful for simulating from models reported in the literature. An example is below.
sim2 <- PM_sim$new(poppar = run2$final, data = dat, model = mod1,...)

Now you must include all three of the mandatory arguments: model, data, and poppar. In this case we used the PM_final() object from a different run (run2) as the poppar, but we could also specify poppar as a list, for example. See SIMrun() for details on constructing a manual poppar.

poppar <- list(wt = 1, mean = c(0.7, 100), covar = diag(c(0.25, 900)))

Under the hood, both of these calls to the simulator use the Legacy SIMrun() function, and thus all its arguments are available when using PM_result$sim(...) or PM_sim$new(...).

Model and data details

The structures of the model and data objects when used by the simulator are identical to those used by NPAG and IT2B. Of course, the model primary (random) parameters must match the parameters in the poppar argument. Any covariates must match between model and data. The data object contains the template dosing and observation history as well as any covariates. Observation values (the OUT column) for EVID=0 events can be any number; they will be replaced with the simulated values. However, do not use -99, as this will simulate a missing value, which might be useful if you are testing the effects of such values. A good choice for the OUT value in the simulator data template is -1 to remind you that it is being simulated, but this choice is optional.

You can have any number of subject records within a data object, each with its own covariate values if applicable. Each subject will cause the simulator to run one time, generating as many simulated profiles as you specify from each template subject. This is controlled by the include, exclude, and nsim arguments to the simulator (see below). The first two specify which subjects in the data object will serve as templates for simulation. The second specifies how many profiles are to be generated from each included subject.

Simulation options

Details of all arguments available to modify simulations are available by typing ?SIMrun into the R console or reading the documentation for SIMrun(). A few are highlighted here.

Simulation from a non-parametric prior distribution (from NPAG) can be done in one of two ways. The first is simply to take the mean and covariance matrix of the distribution and perform a standard Monte Carlo simulation. This is accomplished by setting split = FALSE as an argument.

The second way is what we call semi-parametric (Goutelle et al. 2022). In this method, the non-parametric “support points” in the population model, each a vector of one value for each parameter in the model and the associated probability of that set of parameter values, serve as the mean of one multi-variate normal distribution in a multi-modal, multi-variate joint distribution. The weight of each multi-variate distribution is equal to the probability of the point. The overall population covariance matrix is divided by the number of support points and applied to each distribution for sampling.

Below are illustrations of simulations with a single mean and covariance for two parameters (left, split = FALSE), and with multi-modal sampling (right, split = TRUE).

Limits may be specified for truncated parameter ranges to avoid extreme or inappropriately negative values. The simulator will report values for the total number of simulated profiles needed to generate nsim profiles within the specified limits, as well as the means and standard deviations of the simulated parameters to check for simulator accuracy.

Output from the simulator can be controlled by further arguments passed to SIMrun(). If makecsv is supplied, a .csv file with the simulated profiles will be created with the name as specified by makecsv; otherwise, there will be no .csv file created. If outname is supplied, the simulated values and parameters will be saved in a .txt file whose name is that specified by outname; otherwise the filename will be “simout”. In either case, integers 1 to the number of subjects will be appended to outname or “simout”, e.g. “simout1.txt”, “simout2.txt”.

Simulation output

In R6, simulation output files (e.g. simout1.txt, simout2.txt) are automatically read by SIMparse() and returned as the new PM_sim() object that was assigned to contain the results of the simulation. All files will remain on the hard drive. You no longer need to use SIMparse() as Pmetrics will do it for you. This is unlike the Legacy requirement for you to specifically read the output files from the simulator with SIMparse().

If you simulated from a template with multiple subjects, you will have a simulation output from each template, and each will have nsim profiles. If you wish to combine all the simulations into one PM_sim() object, use the argument combine in your call to the simulator run methods. It will be passed to SIMparse() during the post-simulation processing.

So if our data template has 3 subjects…

sim1 <- run1$sim(data = dat) #sim1 is a PM_sim with 3 simulation results
sim2 <- run1$sim(data = dat, combine = TRUE) #sim2 is a PM_sim with combined results

PM_sim() objects have 6 data fields and 6 methods.

Data fields

Data fields are accessed with $, e.g. sim1$obs.

  • amt A data frame with the amount of drug in each compartment for each template subject at each time.
  • obs A data frame with the simulated observations for each subject, time and output equation.
  • parValues A data frame with the simulated parameter values for each subject.
  • totalCov The covariance matrix of all simulated parameter values, including those that were discarded to be within any limits specified.
  • totalMeans The means of all simulated parameter values, including those that were discarded to be within any limits specified.
  • totalSets The number of simulated sets of parameter values, including those that were discarded to be within any limits specified. This number will always be ≥ nsim.

All of these are detailed further in SIMparse().


Methods are accessed with $ and include parentheses to indicate a function, possibly with arguments e.g. sim1$auc() or sim1$save("sim1.rds").

  • auc Call makeAUC() to calculate the area under the curve from the simulated data.
  • pta Call makePTA() to perform a probability of target attainment analysis with the simulated data.
  • plot Call plot.PM_sim() to plot the simulated data.
  • summary Summarize any data field with or without grouping.
  • save Save the simulation to your hard drive as an .rds file. Load a previous one by providing the filename as the argument to PM_sim$new().
  • clone Copy a simulation result.

All of these are detailed further in PM_sim().To apply them to one of the simulations when there are multiple, use at followed by additional arguments to the method to choose the simulation.

sim1$plot(at = 2,...)


Goutelle, Sylvain, Jean‐Baptiste Woillard, Michael Neely, Walter Yamada, and Laurent Bourguignon. 2022. “Nonparametric Methods in Population Pharmacokinetics.” The Journal of Clinical Pharmacology 62 (2): 142–57.