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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,
  initial_outcome_mean = 5,
  initial_outcome_sd = 2,
  max_visits = 50,
  seed = NULL,
  link = "identity"
)

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).

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.

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{
# Simulate data with default parameters
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
)
# }