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Simulate SensIAT Data

Source: R/simulate_SensIAT_data.R
simulate_SensIAT_data.Rd

Generates simulated longitudinal data following the SensIAT model structure, alternating between observation time generation (Cox proportional hazards model) and outcome generation.

Usage

simulate_SensIAT_data(
  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,
  baseline_outcome_fn = NULL,
  initial_outcome_mean = 5,
  initial_outcome_sd = 2,
  max_visits = 50,
  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.

baseline_outcome_fn

Optional function to generate baseline outcome value for each subject.

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).

seed

Random seed for reproducibility.

Link function for outcome model. One of "identity", "log", or "logit". Determines the scale on which the outcome model operates.

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 columns:

  • Subject_ID - Subject identifier

  • Time - Observation time

  • Outcome - Observed outcome value

  • Additional columns may be added for internal use

Examples

# \donttest{
# Default usage (uses exponential gaps derived from intensity_coef and baseline_hazard)
sim_data <- simulate_SensIAT_data(
    n_subjects = 100,
    End = 830,
    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
)

# Example with custom intensity function and thinning (Exp(lambda_star))
intensity_fn <- function(t, prev_outcome, visit_num) {
  lambda0 <- 0.005
  gamma  <- -0.05
  lambda0 * exp(gamma * prev_outcome)
}
sim_data2 <- simulate_SensIAT_data(
    n_subjects = 50,
    End = 200,
    seed = 123,
    intensity_fn = intensity_fn,
    intensity_bound = 0.05,
    max_visits = 20
)

# Example using a fitted single-index outcome model to generate outcomes
# via the fitted conditional distribution.
#
# Note: this example uses a fitted model object and is intended for
# parametric bootstrap-style simulation.
#
# 
# 
# outcome_model <- fit_SensIAT_single_index_fixed_coef_model(
#     Outcome ~ prev_outcome + time + delta_time,
#     data = training_data,
#     id = Subject_ID
# )
# sim_data3 <- simulate_SensIAT_data(
#     n_subjects = 50,
#     End = 200,
#     seed = 123,
#     outcome_model = outcome_model,
#     intensity_fn = intensity_fn,
#     intensity_bound = 0.05,
#     max_visits = 20
# )
# }