def test_error_no_fit(self, data):
sgf = SurvivalGFormula(data,
idvar='id',
exposure='art',
outcome='d',
time='t')
with pytest.raises(ValueError):
sgf.plot(treatment='all')
def causal_check():
# Check IPTW plots
data = load_sample_data(False)
data[['cd4_rs1', 'cd4_rs2']] = spline(data,
'cd40',
n_knots=3,
term=2,
restricted=True)
data[['age_rs1', 'age_rs2']] = spline(data,
'age0',
n_knots=3,
term=2,
restricted=True)
ipt = IPTW(data, treatment='art', stabilized=True)
ipt.regression_models(
'male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0')
ipt.fit()
ipt.plot_love()
plt.tight_layout()
plt.show()
ipt.plot_kde()
plt.show()
ipt.plot_kde(measure='logit')
plt.show()
ipt.plot_boxplot()
plt.show()
ipt.plot_boxplot(measure='logit')
plt.show()
# Check SurvivalGFormula plots
df = load_sample_data(False).drop(columns=['cd4_wk45'])
df['t'] = np.round(df['t']).astype(int)
df = pd.DataFrame(np.repeat(df.values, df['t'], axis=0),
columns=df.columns)
df['t'] = df.groupby('id')['t'].cumcount() + 1
df.loc[((df['dead'] == 1) & (df['id'] != df['id'].shift(-1))), 'd'] = 1
df['d'] = df['d'].fillna(0)
df['t_sq'] = df['t']**2
df['t_cu'] = df['t']**3
sgf = SurvivalGFormula(df,
idvar='id',
exposure='art',
outcome='d',
time='t')
sgf.outcome_model(
model='art + male + age0 + cd40 + dvl0 + t + t_sq + t_cu')
sgf.fit(treatment='all')
sgf.plot()
plt.show()
sgf.plot(c='r', linewidth=3, alpha=0.8)
plt.show()
def causal_check():
data = load_sample_data(False).drop(columns=['cd4_wk45'])
data[['cd4_rs1', 'cd4_rs2']] = spline(data,
'cd40',
n_knots=3,
term=2,
restricted=True)
data[['age_rs1', 'age_rs2']] = spline(data,
'age0',
n_knots=3,
term=2,
restricted=True)
# Check TimeFixedGFormula diagnostics
g = TimeFixedGFormula(data, exposure='art', outcome='dead')
g.outcome_model(
model=
'art + male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0'
)
g.run_diagnostics(decimal=3)
# Check IPTW plots
ipt = IPTW(data, treatment='art', outcome='dead')
ipt.treatment_model(
'male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0',
stabilized=True)
ipt.marginal_structural_model('art')
ipt.fit()
ipt.plot_love()
plt.tight_layout()
plt.show()
ipt.plot_kde()
plt.show()
ipt.plot_kde(measure='logit')
plt.show()
ipt.plot_boxplot()
plt.show()
ipt.plot_boxplot(measure='logit')
plt.show()
ipt.run_diagnostics()
# Check AIPTW Diagnostics
aipw = AIPTW(data, exposure='art', outcome='dead')
aipw.exposure_model(
'male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0')
aipw.outcome_model(
'art + male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0'
)
aipw.fit()
aipw.run_diagnostics()
aipw.plot_kde(to_plot='exposure')
plt.show()
aipw.plot_kde(to_plot='outcome')
plt.show()
aipw.plot_love()
plt.show()
# Check TMLE diagnostics
tmle = TMLE(data, exposure='art', outcome='dead')
tmle.exposure_model(
'male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0')
tmle.outcome_model(
'art + male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0'
)
tmle.fit()
tmle.run_diagnostics()
tmle.plot_kde(to_plot='exposure')
plt.show()
tmle.plot_kde(to_plot='outcome')
plt.show()
tmle.plot_love()
plt.show()
# Check SurvivalGFormula plots
df = load_sample_data(False).drop(columns=['cd4_wk45'])
df['t'] = np.round(df['t']).astype(int)
df = pd.DataFrame(np.repeat(df.values, df['t'], axis=0),
columns=df.columns)
df['t'] = df.groupby('id')['t'].cumcount() + 1
df.loc[((df['dead'] == 1) & (df['id'] != df['id'].shift(-1))), 'd'] = 1
df['d'] = df['d'].fillna(0)
df['t_sq'] = df['t']**2
df['t_cu'] = df['t']**3
sgf = SurvivalGFormula(df,
idvar='id',
exposure='art',
outcome='d',
time='t')
sgf.outcome_model(
model='art + male + age0 + cd40 + dvl0 + t + t_sq + t_cu')
sgf.fit(treatment='all')
sgf.plot()
plt.show()
sgf.plot(c='r', linewidth=3, alpha=0.8)
plt.show()
'age0',
n_knots=3,
term=2,
restricted=True)
sgf = SurvivalGFormula(df.drop(columns=['dead']),
idvar='id',
exposure='art',
outcome='d',
time='t')
sgf.outcome_model(model='art + male + age0 + age_rs1 + age_rs2 + cd40 + '
'cd4_rs1 + cd4_rs2 + dvl0 + t + t_rs1 + t_rs2 + t_rs3',
print_results=False)
sgf.fit(treatment='all')
sgf.plot(c='b')
sgf.fit(treatment='none')
sgf.plot(c='r')
plt.ylabel('Probability of death')
plt.tight_layout()
plt.savefig("../images/survival_gf_cif.png", format='png', dpi=300)
plt.close()
#####################################################################################################################
# Time-Varying Exposure
#####################################################################################################################
print("Running time-varying exposures...")
import numpy as np
import pandas as pd
from lifelines import KaplanMeierFitter