This is a simulated dataset used to illustrate Bayesian dynamic borrowing in the case when borrowing from an external control arm with a time-to-event endpoint, where the baseline covariate distributions of the internal and external data are balanced via inverse probability weighting.
int_tte_dfint_tte_dfA data frame with 160 rows and 10 columns:
Unique subject ID
Response (observed time at which the participant either had an event or was censored)
Enrollment time
Time from study start
Event indicator (1: event; 0: censored)
Treatment indicator, where 0 = control and 1 = active treatment
Covariate 1, which is normally distributed around 62 with a SD of 8
Covariate 2, which is binary (0 vs. 1) with about 40% of participants having level 1
Covariate 3, which is binary (0 vs. 1) with about 40% of participants having level 1
Covariate 4, which is binary (0 vs. 1) with about 60% of participants having level 1