def test_logrank_veterans():
X, y = load_veterans_lung_cancer()
chisq, pval, stats, covar = compare_survival(y,
X.loc[:, "Celltype"],
return_stats=True)
expected_stats = pandas.DataFrame(
columns=["counts", "observed", "expected", "statistic"],
index=["adeno", "large", "smallcell", "squamous"])
expected_stats.index.name = "group"
expected_stats["counts"] = numpy.array([27, 27, 48, 35], dtype=numpy.intp)
expected_stats["observed"] = numpy.array([26, 26, 45, 31],
dtype=numpy.int_)
expected_stats["expected"] = [
15.6937646143605, 34.5494783863493, 30.1020793268148, 47.6546776724754
]
expected_stats[
"statistic"] = expected_stats["observed"] - expected_stats["expected"]
assert_frame_equal(stats, expected_stats)
expected_var = numpy.array([[
12.9661700605014, -4.07011754397142, -4.40872930298506,
-4.48732321354496
],
[
-4.07011754397142, 24.1990352938484,
-7.81168661717217, -12.3172311327048
],
[
-4.40872930298506, -7.81168661717217,
21.7542679406138, -9.53385202045655
],
[
-4.48732321354496, -12.3172311327048,
-9.53385202045655, 26.3384063667063
]])
assert_almost_equal(covar, expected_var)
expected_chisq = 25.4037003457854
expected_pval = 1.27124593900609e-05
assert round(abs(chisq - expected_chisq), 6) == 0
assert round(abs(pval - expected_pval), 6) == 0
def veterans():
return load_veterans_lung_cancer()
"""
https://scikit-survival.readthedocs.io/en/stable/user_guide/00-introduction.html
"""
from sksurv.datasets import load_veterans_lung_cancer
data_x, data_y = load_veterans_lung_cancer()
data_y
import pandas as pd
pd.DataFrame.from_records(data_y[[11, 5, 32, 13, 23]], index=range(1, 6))
%matplotlib inline
import matplotlib.pyplot as plt
from sksurv.nonparametric import kaplan_meier_estimator
time, survival_prob = kaplan_meier_estimator(data_y["Status"], data_y["Survival_in_days"])
plt.step(time, survival_prob, where="post")
plt.ylabel("est. probability of survival $\hat{S}(t)$")
plt.xlabel("time $t$")
data_x["Treatment"].value_counts()
for treatment_type in ("standard", "test"):
mask_treat = data_x["Treatment"] == treatment_type
time_treatment, survival_prob_treatment = kaplan_meier_estimator(
data_y["Status"][mask_treat],
data_y["Survival_in_days"][mask_treat])
def test_load_veterans_lung_cancer():
x, y = sdata.load_veterans_lung_cancer()
assert x.shape == (137, 6)
assert y.shape == (137,)
assert_structured_array_dtype(y, 'Status', 'Survival_in_days', 128)
def plot_cumulative_dynamic_auc(risk_score, label, color=None):
auc, mean_auc = cumulative_dynamic_auc(y_train, y_test, risk_score, times)
plt.plot(times, auc, marker="o", color=color, label=label)
plt.xlabel("days from enrollment")
plt.ylabel("time-dependent AUC")
plt.axhline(mean_auc, color=color, linestyle="--")
plt.legend()
for i, col in enumerate(num_columns):
plot_cumulative_dynamic_auc(x_test[:, i], col, color="C{}".format(i))
ret = concordance_index_ipcw(y_train, y_test, x_test[:, i], tau=times[-1])
from sksurv.datasets import load_veterans_lung_cancer
va_x, va_y = load_veterans_lung_cancer()
cph = make_pipeline(OneHotEncoder(), CoxPHSurvivalAnalysis())
cph.fit(va_x, va_y)
va_times = np.arange(7, 183, 7)
# estimate performance on training data, thus use `va_y` twice.
va_auc, va_mean_auc = cumulative_dynamic_auc(va_y, va_y, cph.predict(va_x),
va_times)
plt.plot(va_times, va_auc, marker="o")
plt.axhline(va_mean_auc, linestyle="--")
plt.xlabel("days from enrollment")
plt.ylabel("time-dependent AUC")
plt.grid(True)
