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Compute MM-optimal Sample Times

Source: R/MMopt.R
MM_opt.Rd

[Stable]

Computes 1 or more MM-optimal sampling times.

Usage

MM_opt(
  poppar,
  model,
  data,
  nsamp = 1,
  weight = list(none = 1),
  predInt = 0.5,
  mmInt,
  outeq = 1,
  clean = TRUE,
  ...
)

Arguments

poppar

A PM_result object created by loading results of a prior NPAG run with PM_load or a PMfinal object made in legacy Pmetrics.

model

If poppar is a PM_result, the embedded model will be used and this argument is not required. For a legacy PMfinal poppar, model should be the name of a suitable model file in the working directory. The default is "model.txt".

data

Either a PM_data object or character vector with the filename of a Pmetrics data file that contains template regimens and observation times. The value for outputs can be coded as any number(s) other than -99. The number(s) will be replaced in the simulator output with the simulated values.

nsamp

The number of MM-optimal sample times to compute; default is 1, but can be any number. Values >4 will take an exponentially longer time.

weight

List whose names indicate the type of weighting, and values indicate the relative weight. Values should sum to 1. Names can be any of the following:

  • none The default. MMopt times will be chosen to maximally discriminate all responses at all times.

  • AUC MMopt times will be chosen to maximally discriminate AUC, regardless of the shape of the response profile.

  • max MMopt times will be chosen to maximally discriminate maximum, regardless of the shape of the response profile.

  • min MMopt times will be chosen to maximally discriminate minimum, regardless of the shape of the response profile.

Any combination of AUC, max, and min can be chosen. If "none" is specified, other weight types will be ignored and the relative value will be set to 1. For example,list(auc = 0.5,max = 0.5) or list(auc = 0.2, min = 0.8). The default is list(none = 1).

predInt

The interval in fractional hours for simulated predicted outputs at times other than those specified in the template data. The default is 0.5, which means there will be simulated outputs every 30 minutes from time 0 up to the maximal time in the template file. You may also specify predInt as a vector of 3 values, e.g. c(1,4,1), similar to the R command seq, where the first value is the start time, the second is the stop time, and the third is the step value. Outputs for times specified in the template file will also be simulated. To simulate outputs only at the output times in the template data (i.e. EVID=0 events), use predInt = 0. Note that the maximum number of predictions total is 594, so the interval must be sufficiently large to accommodate this for a given number of output equations and total time to simulate over. If predInt is set so that this cap is exceeded, predictions will be truncated.

mmInt

Specify the time intervals from which MMopt times can be selected. These should only include simulated times specified by predInt.

outeq

Output equation to optimize

clean

Boolean parameter to specify whether temporary files made in the course of the simulation run should be deleted. Defaults to TRUE. This is primarily used for debugging.

...

Other parameters to pass to SIMrun, which are not usually necessary.

Value

A object of class MMopt with 3 items.

sampleTime

The MM-optimal sample times

bayesRisk

The Bayesian risk of mis-classifying a subject based on the sample times. This is more useful for comparisons between sampling strategies, with minimization the goal.

simdata

A PM_sim object with the simulated profiles

mmInt

A list with the mmInt values, NULL if mmInt is missing.

Details

Based on the mulitple-model optimization algorithm. Bayard, David S., and Michael Neely. "Experiment Design for Nonparametric Models Based on Minimizing Bayes Risk: Application to Voriconazole." Journal of Pharmacokinetics and Pharmacodynamics 44, no. 2 (April 2017): 95–111. https://doi.org/10.1007/s10928-016-9498-5.

See also

SIMrun, PM_sim, plot.MMopt, print.MMopt

Author

Michael Neely