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Simulate Treatment and Control Groups

Source: R/simulate_SensIAT_data.R
simulate_SensIAT_two_groups.Rd

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"
)

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

Value

A tibble with an additional Treatment column indicating group assignment.

Examples

# \donttest{
# Simulate data with treatment effect
sim_data <- simulate_SensIAT_two_groups(
    n_subjects = 100,
    End = 830,
    treatment_effect = 1.5,
    treatment_intensity_effect = 0.9
)
# }