A simulated dataset with time-varying and baseline variables for subjects with a survival outcome, suitable for use with the g-formula and mediation functions under survival settings.
Format
A data frame with 7113 rows and 10 variables:
- id
Unique subject identifier.
- time
Time variable (integer, starting at 0).
- V
Time-fixed baseline covariate.
- L
Time-varying confounder.
- A
Time-varying binary exposure.
- M
Time-varying mediator.
- Y
Survival outcome indicator (1 = event, 0 = alive/censored).
- lag1_A
Lagged exposure (A at previous time point).
- lag1_M
Lagged mediator.
- lag1_L
Lagged confounder.
Details
The data-generating structure can be summarized as: $$ A_t \leftarrow V, L_{t-1}, A_{t-1}, t;\quad L_t \leftarrow V, A_t, L_{t-1}, t;\quad M_t \leftarrow V, A_t, L_t, M_{t-1}, t;\quad Y_t \leftarrow V, A_t, M_t, L_t, A_t*M_t, t. $$
Events and follow-up observations are generated only while subjects remain at
risk. Therefore, once Y becomes 1 at time \(t\), no observations are
retained for that subject at subsequent time points \(t+1, t+2, \ldots\).