In ruminate: A Pharmacometrics Data Transformation and Analysis Tool

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 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:

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.
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 within the "manual"action type.

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: