Simulate outcomes from additive linear models — simulate_outcome_from

Generates longitudinal outcomes from a structural causal model of the form `Y_t = A * effect + X phase (`t <= T_cross`), treatment assignment `A` is used directly. In the OLE phase (`t > T_cross`), all RCT patients are assumed to receive treatment (effect multiplied by 1 instead of `A`).

Usage

simulate_outcome_from_model(X, A, outcome_model_specs, OLE_flag, T_cross)

Arguments

X: Data frame of covariates.
A: Numeric vector of treatment indicators (same length as `nrow(X)`).
outcome_model_specs: List of lists, one per time point. See Details above.
OLE_flag: Logical. If `TRUE`, time points after `T_cross` use the OLE model (all patients treated).
T_cross: Positive integer. The crossover time point separating the primary and OLE phases. Only used when `OLE_flag = TRUE`.

Value

A data frame with `n` rows and one column per time point (`y1`, `y2`, ...).

Details

Each element of `outcome_model_specs` is a list with:

`effect`: Numeric scalar. Treatment effect for this time point.
`model_form_x`: Named numeric vector of covariate coefficients. Names must include `"1"` (intercept) and match column names in `X`.
`noise_mean`: Numeric scalar. Mean of the normal noise.
`noise_sd`: Numeric scalar. SD of the normal noise.

Examples

X <- data.frame(x1 = rnorm(20), x2 = rnorm(20))
A <- rbinom(20, 1, 0.5)
specs <- list(
  list(
    effect = 1.5,
    model_form_x = c("1" = 2.0, "x1" = 0.5, "x2" = -0.3),
    noise_mean = 0, noise_sd = 1
  ),
  list(
    effect = 0,
    model_form_x = c("1" = 1.0, "x1" = 0.2, "x2" = 0.1),
    noise_mean = 0, noise_sd = 1
  )
)
Y <- simulate_outcome_from_model(X, A, specs, OLE_flag = FALSE, T_cross = 2)