mk_subjects: Fetches Information from an rxode2 Object

In ruminate: A Pharmacometrics Data Transformation and Analysis Tool

Fetches Information from an rxode2 Object

Description

This will provide information like parameter names, covriates, etc from an rxode2 object.

Usage

mk_subjects(object, nsub = 10, covs = NULL)

Arguments

object	rxode2 model object An ID string that corresponds with the ID used to call the modules UI elements.
nsub	Number of subjects to generate. If set to 1 it will return the typical values (IIV set to zero).
covs	List describing how covariates should be generated.

Details

See below.

The underlying simulations are run using rxode2, and as such we need an rxode2 system object. From that we can either simulate subjects or load them from a file. Next we need to define a set of rules. These will be a set of conditions and actions. At each evaluation time point the conditions are evaluated. When a condition is met the actions associated with that condition are executed. For example, if during a visit (an evaluation time point) the trough PK is below a certain level (condition) we may want to increase the dosing regimen for the next dosing cycle (action).

Creating subjects

Subjects are expected in a data frame with the following column headers:

• id Individual subject id

• Names of parameters and iiv as specified in the ini section of the rxode2 function specification

• Names of covariates used in the model.

mk_subjects() — Creates subjects for simulation by sampling based on between-subject variability and generating covariate information based on user specifications.

Covariates

The covs input is a list with the following structure:

• type: Can be either “fixed”, “discrete”, or “continuous”.

• sampling: This field is only needed for a “continuous” covariate ’ type and can be either “random”, “normal” or “log-normal”.

• values: This field depends on the type and optional sampling above.

  • fixed: A single value.

  • discrete: A vector of possible discrete elements.

  • continuous, random: Two values the first is the lower bound and the second is the upper bound.

  • continuous, normal: Two values the first is the mean and the second is the variance.

  • continuous, log-normal: Two values the first is the mean and the second is the variance.

This examples shows the SEX_ID randomly sampled from the values specified, SUBTYPE_ID fixed at a value, and WT sampled from a log-normal distribution.

covs = list(
  SEX_ID     = list(type     = "discrete", 
                    values   = c(0,1)),
  SUBTYPE_ID = list(type     = "fixed",
                    values   = c(0)),
  WT         = list(type     = "continuous",
                    sampling = "log-normal",
                    values   = c(70, .15))
)

Rule-based simulations

simulate_rules() — This will run simulations based on the rule definitions below.

Rules

Rules are a named list where the list name can be a short descriptive label used to remember what the rule does. These names will be returned as columns in the simulated data frame.

• condition: Character string that evaluates to either TRUE or FALSE. When true the action portion will be triggered. For a list of objects available see the Rule-evaluation environment below.

• fail_flag: Flag set in the rule_id column when the condition is not met (set to "false" if not specified).

• true_flag: Flag set in the rule_id column when the condition is met (set to "true" if not specified).

• action: This is what the rule will trigger can be any of the following:

  • type: This defines the action type and can be either "dose", "set state", or "manual".

Based on the type the action field will expect different elements.

Dosing:

• action

  • type: "dose"

  • values: Character string that evaluates as a numeric vector dosing amounts (e.g. "c(3, 3, 3, 3)")

  • times: Character string that evaluates as a numeric vector of times (e.g. "c(0, 14, 28, 42)")

  • durations: Character string that evaluates as a numeric vector of durations (e.g. "c(0, 0, 0, 0)", zero for bolus dosing)

Changing a state value:

• action

  • type: "set state"

  • state: Character string with the name of the state to set ("Ac")

  • value: Character string that evaluates as a numeric value for state (e.g. "Ac/2" would set the state to half the value of Ac at the evaluation point)

Manual modification of the simulation:

• action

  • type: "manual"

  • code: Character string of code to evaluate.

Rule-evaluation environment

Beyond simple simulations it will be necessary to execute actions based on the current or previous state of the system. For this reason, when a condition or elements of the action (e.g., the values, times and durations of a dose action type) are being evaluated, the following objects will be available at each evaluation point:

• outputs: The value of each model output.

• states: The value of each named state or compartment.

• covariates: The value of each named covariate.

• subject-level parameters: The value of each named parameter.

• rule value: The last value the rule evaluated as.

• id: Current subject id.

• time: Current evaluation time.

• SI_SUB_HISTORY: A data frame of the simulation history of the current subject up to the current evaluation point.

• SI_subjects: The subjects data frame.

• SI_eval_times: Vector of the evaluation times.

• SI_interval_ev: The events table in it’s current state for the given simulation interval.

• SI_ev_history: This is the history of the event table containing all the events leading up to the current interval.

• SI_ud_history: This is a free form object the user can define or alter within the “manual”action type (ud-user defined, history).

The following functions will be available:

• SI_fpd: This function will fetch the previous dose (fpd) for the given id and state. For example for the current id and the state Ac you would do the following:

SI_fpd(id=id, state="Ac")

Time scales

You can include columns in your output for different time scales if you wish. You need to create a list in the format below. One element should be system with a short name for the system time scale. The next should be details which is a list containing short names for each time scale you want to include. Each of these is a list with a verbose name for the time scale (verb) and a numerical conversion indicating how that time scale relates to the others. Here we define weeks and days on the basis of seconds.

time_scales = list(system="days", 
                details= list(
                  weeks = list(verb="Weeks",    conv=1/(60*60*24*7)),
                  days  = list(verb="Days",     conv=1/(60*60*24))))

Value

List with the following elements.

• isgood: Return status of the function.

• msgs: Error or warning messages if any issues were encountered.

• subjects: Data frame of parameters and covariates for the subjects generated.

• iCov: Data frame of the covariates.

• params: Data frame of the parameters.

See Also

vignette("clinical_trial_simulation", package = "ruminate")

Examples

library(formods)
library(ggplot2)

# For more information see the Clinical Trial Simulation vignette:
# https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html

# None of this will work if rxode2 isn't installed:
if(is_installed("rxode2")){
library(rxode2)
set.seed(8675309)
rxSetSeed(8675309)

my_model = function ()
{
    description <- "One compartment PK model with linear clearance using differential equations"
    ini({
        lka <- 0.45
        label("Absorption rate (Ka)")
        lcl <- 1
        label("Clearance (CL)")
        lvc <- 3.45
        label("Central volume of distribution (V)")
        propSd <- c(0, 0.5)
        label("Proportional residual error (fraction)")
        etalcl ~ 0.1
    })
    model({
        ka <- exp(lka)
        cl <- exp(lcl + etalcl)
        vc <- exp(lvc)
        kel <- cl/vc
        d/dt(depot) <- -ka * depot
        d/dt(central) <- ka * depot - kel * central
        Cc <- central/vc
        Cc ~ prop(propSd)
    })
}

# This creates an rxode2 object
object  = rxode(my_model)

# If you want details about the parameters, states, etc
# in the model you can use this:
rxdetails = fetch_rxinfo(object)

rxdetails$elements

# Next we will create subjects. To do that we need to
# specify information about covariates:
nsub = 2
covs = list(
  WT         = list(type     = "continuous",
                    sampling = "log-normal",
                    values   = c(70, .15))
)

subs = mk_subjects(object = object,
                   nsub   = nsub,
                   covs   = covs)

head(subs$subjects)

rules = list(
  dose = list(
    condition = "TRUE",
    action    = list(
      type  = "dose",
      state     = "central",
      values    = "c(1)",
      times     = "c(0)",
      durations = "c(0)")
    )
)

# We evaulate the rules for dosing at time 0
eval_times =  0

# Stop 2 months after the last dose
output_times = seq(0, 56, 1)

# This runs the rule-based simulations
simres =
  simulate_rules(
    object        = object,
    subjects      = subs[["subjects"]],
    eval_times    = eval_times,
    output_times  = output_times,
    rules         = rules)

# First subject data:
sub_1 = simres$simall[simres$simall$id == 1, ]

# First subjects events
evall = as.data.frame(simres$evall)
ev_sub_1 = evall[evall$id ==1, ]

# All of the simulation data
simall = simres$simall
simall$id = as.factor(simall$id)

# Timecourse
psim =
  plot_sr_tc(
    sro    = simres,
    dvcols = "Cc")
psim$fig

# Events
pev =
  plot_sr_ev(
    sro    = simres,
    ylog   = FALSE)
pev$fig

}

ruminate documentation built on Nov. 5, 2025, 5:34 p.m.