Simulate a two-arm longitudinal trial

Multivariate-normal responses at n_visits post-baseline visits with an AR(1) correlation, a baseline covariate, a treatment effect growing linearly to delta at the last visit, and dropout after each visit whose probability rises as the current value falls (missing at random).

Usage

simulate_trial(
  n_per_arm = 50,
  delta = 0,
  n_visits = 4,
  sigma = 10,
  rho = 0.6,
  dropout = 0.08,
  baseline_beta = 0.5,
  errors = c("normal", "t"),
  t_df = 3,
  hetero = 1,
  mnar = 0,
  interaction = 0,
  seed = NULL
)

Arguments

n_per_arm

Subjects per arm.

delta

Treatment effect at the last visit (active minus reference).

n_visits

Number of post-baseline visits.

sigma

Residual standard deviation at each visit.

rho

AR(1) correlation between adjacent visits.

dropout

Baseline per-visit dropout probability.

baseline_beta

Slope of the response on the baseline covariate.

errors

"normal" or "t" (Student t with t_df degrees of freedom, scaled to the same standard deviation: heavy tails).

t_df

Degrees of freedom for errors = "t".

hetero

Multiplier of the residual standard deviation at the last visit relative to the first (1 = homoscedastic); the SD grows linearly.

mnar

Strength of missing-not-at-random dropout: dropout after a visit depends on the value the subject would have had at the NEXT visit (0 = missing at random, as the default).

interaction

Treatment effect modification by the baseline covariate: the effect at the last visit is delta * (1 + interaction * (base - 50) / 10).

seed

Random seed.

Value

A long data frame with id, arm ("REF", "ACT"), base, visit ("V1", ...) and y, missing rows removed.

Examples

d <- simulate_trial(40, delta = 3, seed = 1); head(d)
#>      id arm     base visit          y
#> 1 S0001 REF 43.73546    V1 -8.8189564
#> 2 S0001 REF 43.73546    V2 -3.1434784
#> 3 S0001 REF 43.73546    V3  2.5194907
#> 4 S0001 REF 43.73546    V4 10.7219989
#> 5 S0002 REF 51.83643    V1 -0.4335695
#> 9 S0003 REF 41.64371    V1  7.6027269