def logrank_test(event_times_A, event_times_B, event_observed_A=None, event_observed_B=None,
                 alpha=0.95, t_0=-1, **kwargs):

    event_times_A, event_times_B = np.array(event_times_A), np.array(event_times_B)
    if event_observed_A is None:
        event_observed_A = np.ones(event_times_A.shape[0])
    if event_observed_B is None:
        event_observed_B = np.ones(event_times_B.shape[0])

    event_times = np.r_[event_times_A, event_times_B]
    groups = np.r_[np.zeros(event_times_A.shape[0], dtype=int), np.ones(event_times_B.shape[0], dtype=int)]
    event_observed = np.r_[event_observed_A, event_observed_B]
    return multivariate_logrank_test(event_times, groups, event_observed,
                                     alpha=alpha, t_0=t_0, **kwargs)
Measures and reports on whether two intensity processes are different. That is, given two
dataset/ETHPy150Open CamDavidsonPilon/lifelines/lifelines/statistics.py/logrank_test