def test_survival_difference_at_fixed_point_in_time_test(): df = load_waltons() ix = df["group"] == "miR-137" waltonT1 = df.loc[ix]["T"] waltonT2 = df.loc[~ix]["T"] result = stats.survival_difference_at_fixed_point_in_time_test(10, waltonT1, waltonT2) assert result.p_value < 0.05
def test_survival_difference_at_fixed_point_in_time_test_parametric(): df = load_waltons() ix = df["group"] == "miR-137" wf1 = WeibullFitter().fit(df.loc[ix]["T"], df.loc[ix]["E"]) wf2 = WeibullFitter().fit(df.loc[~ix]["T"], df.loc[~ix]["E"]) result = stats.survival_difference_at_fixed_point_in_time_test(10, wf1, wf2) assert result.p_value < 0.05
def test_survival_difference_at_fixed_point_in_time_test_nonparametric(): df = load_waltons() ix = df["group"] == "miR-137" kmf1 = KaplanMeierFitter().fit(df.loc[ix]["T"], df.loc[ix]["E"]) kmf2 = KaplanMeierFitter().fit(df.loc[~ix]["T"], df.loc[~ix]["E"]) result = stats.survival_difference_at_fixed_point_in_time_test(10, kmf1, kmf2) assert result.p_value < 0.05
def test_survival_difference_at_fixed_point_in_time_test_interval_censoring(): T1 = np.random.exponential(1e-6, size=1000) T2 = np.random.exponential(1e-6, size=1000) E = T1 > T2 T = np.maximum(T1, T2) wf1 = WeibullFitter().fit_interval_censoring(T, T) wf2 = WeibullFitter().fit_interval_censoring(2 * T, 2 * T) result = stats.survival_difference_at_fixed_point_in_time_test(T.mean(), wf1, wf2) assert result.p_value < 0.05
def test_survival_difference_at_fixed_point_in_time_test_left_censoring(): T1 = np.random.exponential(1e-6, size=1000) T2 = np.random.exponential(1e-6, size=1000) E = T1 > T2 T = np.maximum(T1, T2) kmf1 = KaplanMeierFitter().fit_left_censoring(T) kmf2 = KaplanMeierFitter().fit_left_censoring(2 * T) result = stats.survival_difference_at_fixed_point_in_time_test(T.mean(), kmf1, kmf2) assert result.p_value < 0.05