Simulate Treatment and Control Groups

Generate simulated data for both treatment and control groups with potentially different parameters.

Usage

simulate_SensIAT_two_groups(
  n_subjects,
  End,
  intensity_coef = -0.05,
  outcome_coef = list(prev_outcome = c(0.7, -0.1, 0.05), time = -0.001, delta_time =
    -0.002, intercept = 2),
  baseline_hazard = 0.005,
  outcome_sd = 1.5,
  initial_outcome_mean = 5,
  initial_outcome_sd = 2,
  max_visits = 50,
  treatment_effect = 0,
  treatment_intensity_effect = 1,
  seed = NULL,
  link = "identity",
  intensity_fn = NULL,
  intensity_bound = NULL,
  outcome_model = NULL,
  outcome_simulator = NULL
)

Arguments

n_subjects

Number of subjects to simulate.

End

Maximum follow-up time.

intensity_coef

Coefficient for the effect of previous outcome on observation intensity. Can be a scalar or vector (one per visit number stratum).

outcome_coef

Named list of coefficients for outcome model including:

• prev_outcome - coefficients for natural spline of previous outcome (length 3)

• time - coefficient for time since baseline

• delta_time - coefficient for time since last observation

• intercept - intercept term

baseline_hazard

Baseline hazard function. Either a function of time and visit number, or a numeric value for constant baseline hazard.

outcome_sd

Standard deviation of the outcome residuals.

initial_outcome_mean

Mean of the initial (baseline) outcome.

initial_outcome_sd

Standard deviation of the initial outcome.

max_visits

Maximum number of visits per subject (to prevent infinite loops).

treatment_effect

Additive treatment effect on outcomes (added to intercept on link scale).

treatment_intensity_effect

Multiplicative effect on observation intensity (values < 1 mean fewer observations in treatment group).

seed

Random seed for reproducibility.

link

Link function for outcome model. One of "identity", "log", or "logit".

intensity_fn

Optional function to compute intensity (hazard) of observation. If provided, should take arguments (time, prev_outcome, visit_num) and return a scalar intensity value. If NULL (default), intensity is computed from intensity_coef and baseline_hazard.

intensity_bound

Upper bound on intensity for rejection sampling. Required if intensity_fn is provided. Represents the supremum of the intensity function on the interval of interest.

outcome_model

Optional fitted single-index outcome model. If provided, outcomes for follow-up visits are generated from the fitted model via make_single_index_simulator().

outcome_simulator

Optional simulator function for follow-up outcomes. When provided, it overrides the internal outcome generation function. This function should accept prev_outcome, time, delta_time, and optionally newdata.

Value

A tibble with an additional Treatment column indicating group assignment.

Examples

# \donttest{
# Default treatment/control simulation (uses exponential gaps derived from coefficients)
sim_data <- simulate_SensIAT_two_groups(
    n_subjects = 100,
    End = 830,
    treatment_effect = 1.5,
    treatment_intensity_effect = 0.9
)

# Example using custom intensity with thinning
intensity_fn <- function(t, prev_outcome, visit_num) {
  lambda0 <- 0.005
  gamma  <- -0.05
  lambda0 * exp(gamma * prev_outcome)
}
sim_data2 <- simulate_SensIAT_two_groups(
    n_subjects = 100,
    End = 200,
    seed = 123,
    intensity_fn = intensity_fn,
    intensity_bound = 0.05,
    treatment_effect = 1.0,
    treatment_intensity_effect = 0.9
)
# }