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
