CTS_fetch_ds: Fetch Clinical Trial Simulator Module Datasets

In ruminate: A Pharmacometrics Data Transformation and Analysis Tool

Fetch Clinical Trial Simulator Module Datasets

Description

Fetches the datasets produced by the module. For each cohort this will be the simulation timecourse and the event table

Usage

CTS_fetch_ds(state)

Arguments

state

CTS state from CTS_fetch_state()

Value

Character object vector with the lines of code

list containing the following elements

• isgood: Return status of the function.

• hasds: Boolean indicator if the module has any datasets

• msgs: Messages to be passed back to the user.

• ds: List with datasets. Each list element has the name of the R-object for that dataset. Each element has the following structure:

  • label: Text label for the dataset

  • MOD_TYPE: Short name for the type of module.

  • id: module ID

  • DS: Dataframe containing the actual dataset.

  • DSMETA: Metadata describing DS

  • code: Complete code to build dataset.

  • checksum: Module checksum.

  • DSchecksum: Dataset checksum.

Examples

# 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:

library(formods)
if( Sys.getenv("ruminate_rxfamily_found") == "TRUE"){

# This will populate the session variable with the model building (MB) module
sess_res = MB_test_mksession()
session = sess_res[["session"]]

id     = "CTS"
id_ASM = "ASM"
id_MB  = "MB"
input  = list()

# Configuration files
FM_yaml_file  = system.file(package = "formods", "templates", "formods.yaml")
MOD_yaml_file = system.file(package = "ruminate", "templates", "CTS.yaml")

state = CTS_fetch_state(id              = id,
                        id_ASM          = id_ASM,
                        id_MB           = id_MB,
                        input           = input,
                        session         = session,
                        FM_yaml_file    = FM_yaml_file,
                        MOD_yaml_file   = MOD_yaml_file,
                        react_state     = NULL)


# Fetch a list of the current element
current_ele = CTS_fetch_current_element(state)

# You can modify the element
current_ele[["element_name"]] = "A more descriptive name"

# Defining the source model
state[["CTS"]][["ui"]][["source_model"]] = "MB_obj_1_rx"
current_ele = CTS_change_source_model(state, current_ele)

# Single visit
current_ele[["ui"]][["visit_times"]]                 = "0"
current_ele[["ui"]][["cts_config_nsteps"]]           = "5"

# Creating a dosing rule
state[["CTS"]][["ui"]][["rule_condition"]]           = "time == 0"
state[["CTS"]][["ui"]][["rule_type"]]                = "dose"
state[["CTS"]][["ui"]][["action_dosing_state"]]      = "central"
state[["CTS"]][["ui"]][["action_dosing_values"]]     = "c(1)"
state[["CTS"]][["ui"]][["action_dosing_times"]]      = "c(0)"
state[["CTS"]][["ui"]][["action_dosing_durations"]]  = "c(0)"
state[["CTS"]][["ui"]][["rule_name"]]                = "Single_Dose"

# Adding the rule:
current_ele = CTS_add_rule(state, current_ele)

# Appending the plotting details as well
current_ele[["ui"]][["fpage"]]             = "1"
current_ele[["ui"]][["dvcols"]]            = "Cc"

# Reducing the number of subjects and steps to speed things up on CRAN
current_ele[["ui"]][["nsub"]]              = "2"
current_ele[["ui"]][["cts_config_nsteps"]] = "5"

# Putting the element back in the state forcing code generation
state = CTS_set_current_element(
  state   = state,
  element = current_ele)

# Now we pull out the current element, and simulate it
current_ele = CTS_fetch_current_element(state)
#current_ele = CTS_simulate_element(state, current_ele)

# Next we plot the element
current_ele = CTS_plot_element(state, current_ele)

# Now we save those results back into the state:
state = CTS_set_current_element(
  state   = state,
  element = current_ele)

# This will extract the code for the current module
code = CTS_fetch_code(state)
code

# This will update the checksum of the module state
state = CTS_update_checksum(state)


# Access the datasets generated from simulations
ds = CTS_fetch_ds(state)

# CTS_add_covariate
state[["CTS"]][["ui"]][["covariate_value"]]            = "70, .1"
state[["CTS"]][["ui"]][["covariate_type_selected"]]    = "cont_lognormal"
state[["CTS"]][["ui"]][["selected_covariate"]]         = "WT"
current_ele = CTS_add_covariate(state, current_ele)

# Creates a new empty element
state = CTS_new_element(state)

# Delete the current element
state = CTS_del_current_element(state)
}

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