diff --git a/README.md b/README.md
index 9cc030d..78757fd 100644
--- a/README.md
+++ b/README.md
@@ -45,7 +45,7 @@ The mandatory dependencies of this library are:
Optional dependencies are:
-* [hpstat](https://yingtongli.me/git/hpstat), for *IntervalCensoredCox*
+* [hpstat](https://yingtongli.me/git/hpstat), for *turnbull* and *IntervalCensoredCox*
* [matplotlib](https://matplotlib.org/) and [seaborn](https://seaborn.pydata.org/), for plotting functions
* [mpmath](https://mpmath.org/), for *beta_ratio* and *beta_oddsratio*
* [PyCryptodome](https://www.pycryptodome.org/), for *pickle_write_encrypted* and *pickle_read_encrypted*
@@ -72,7 +72,7 @@ Relevant statistical functions are all directly available from the top-level *yl
* Survival analysis:
* *kaplanmeier*: Kaplan–Meier plot
* *logrank*: Log-rank test
- * *turnbull*: Turnbull estimator plot for interval-censored data
+ * *turnbull*: Turnbull estimator plot, including pointwise confidence intervals, for interval-censored data
* Input/output:
* *pickle_write_compressed*, *pickle_read_compressed*: Pickle a pandas DataFrame and compress using LZMA
* *pickle_write_encrypted*, *pickle_read_encrypted*: Pickle a pandas DataFrame, compress using LZMA, and encrypt
diff --git a/yli/survival.py b/yli/survival.py
index 87bc414..4fba928 100644
--- a/yli/survival.py
+++ b/yli/survival.py
@@ -15,9 +15,14 @@
# along with this program. If not, see .
import numpy as np
+import pandas as pd
from scipy import stats
import statsmodels.api as sm
+import io
+import json
+import subprocess
+
from .config import config
from .sig_tests import ChiSquaredResult
from .utils import Estimate, check_nan
@@ -130,25 +135,27 @@ def calc_survfunc_kaplanmeier(time, status, ci, transform_x=None, transform_y=No
return xpoints, ypoints, None, None
-def turnbull(df, time_left, time_right, by=None, *, step_loc=0.5, transform_x=None, transform_y=None, nan_policy='warn'):
+def turnbull(df, time_left, time_right, by=None, *, ci=True, step_loc=0.5, transform_x=None, transform_y=None, nan_policy='warn', fig=None, ax=None):
"""
Generate a Turnbull estimator plot, which extends the Kaplan–Meier estimator to interval-censored observations
- The intervals are assumed to be half-open intervals, (*left*, *right*]. *right* == *np.inf* implies the event was right-censored. Unlike :func:`yli.kaplanmeier`, times must be given as numeric dtypes and not as pandas timedelta.
+ The intervals are assumed to be half-open intervals, (*left*, *right*]. *right* == *np.inf* implies the event was right-censored.
By default, the survival function is drawn as a step function at the midpoint of each Turnbull interval.
- Uses the Python *lifelines* and *matplotlib* libraries.
+ Uses the hpstat *turnbull* command.
:param df: Data to generate plot for
:type df: DataFrame
- :param time_left: Column in *df* for the time to event, left interval endpoint (numeric)
+ :param time_left: Column in *df* for the time to event, left interval endpoint (numeric or timedelta)
:type time_left: str
- :param time_right: Column in *df* for the time to event, right interval endpoint (numeric)
+ :param time_right: Column in *df* for the time to event, right interval endpoint (numeric or timedelta)
:type time_right: str
:param by: Column in *df* to stratify by (categorical)
:type by: str
- :param step_loc: Proportion along the length of each Turnbull interval to step down the survival function, e.g. 0 for left bound, 1 for right bound, 0.5 for interval midpoint (numeric)
+ :param ci: Whether to plot confidence intervals around the survival function
+ :type ci: bool
+ :param step_loc: Proportion along the length of each Turnbull interval to step down the survival function, e.g. 0 for left bound, 1 for right bound, 0.5 for interval midpoint
:type step_loc: float
:param transform_x: Function to transform x axis by
:type transform_x: callable
@@ -168,7 +175,11 @@ def turnbull(df, time_left, time_right, by=None, *, step_loc=0.5, transform_x=No
else:
df = check_nan(df[[time_left, time_right]], nan_policy)
- fig, ax = plt.subplots()
+ # Covert timedelta to numeric
+ df, time_units = survtime_to_numeric(df, time_left, time_right)
+
+ if ax is None:
+ fig, ax = plt.subplots()
if by is not None:
# Group by independent variable
@@ -176,13 +187,16 @@ def turnbull(df, time_left, time_right, by=None, *, step_loc=0.5, transform_x=No
for group in groups.groups:
subset = groups.get_group(group)
- handle = plot_survfunc_turnbull(ax, subset[time_left], subset[time_right], step_loc, transform_x, transform_y)
+ handle = plot_survfunc_turnbull(ax, subset[time_left], subset[time_right], ci, step_loc, transform_x, transform_y)
handle.set_label('{} = {}'.format(by, group))
else:
# No grouping
- plot_survfunc_turnbull(ax, df[time_left], df[time_right], step_loc, transform_x, transform_y)
+ plot_survfunc_turnbull(ax, df[time_left], df[time_right], ci, step_loc, transform_x, transform_y)
- ax.set_xlabel('Analysis time')
+ if time_units:
+ ax.set_xlabel('{} + {} ({})'.format(time_left, time_right, time_units))
+ else:
+ ax.set_xlabel('{} + {}'.format(time_left, time_right))
ax.set_ylabel('Survival probability')
ax.set_xlim(left=0)
ax.set_ylim(0, 1)
@@ -192,29 +206,39 @@ def turnbull(df, time_left, time_right, by=None, *, step_loc=0.5, transform_x=No
return fig, ax
-def plot_survfunc_turnbull(ax, time_left, time_right, step_loc=0.5, transform_x=None, transform_y=None):
- xpoints, ypoints = calc_survfunc_turnbull(time_left, time_right, step_loc, transform_x, transform_y)
+def plot_survfunc_turnbull(ax, time_left, time_right, ci, step_loc=0.5, transform_x=None, transform_y=None):
+ xpoints, ypoints, ypoints0, ypoints1 = calc_survfunc_turnbull(time_left, time_right, ci, step_loc, transform_x, transform_y)
+
handle = ax.plot(xpoints, ypoints)[0]
+
+ if ci:
+ ax.fill_between(xpoints, ypoints0, ypoints1, alpha=0.3, label='_')
+
return handle
-def calc_survfunc_turnbull(time_left, time_right, step_loc=0.5, transform_x=None, transform_y=None):
- from lifelines.fitters.npmle import npmle
-
- EPSILON = 1e-10
-
- # TODO: Support left == right => failure was exactly observed
-
- followup_left = time_left + EPSILON # Add epsilon to make interval half-open
- followup_right = time_right
-
+def calc_survfunc_turnbull(time_left, time_right, ci, step_loc=0.5, transform_x=None, transform_y=None):
# Estimate the survival function
- #sf = lifelines.KaplanMeierFitter().fit_interval_censoring(followup_left, followup_right)
- # Call lifelines.fitters.npmle.npmle directly so we can compute midpoints, etc.
- sf_probs, turnbull_intervals = npmle(followup_left, followup_right)
+ # Prepare arguments
+ # TODO: Pass through other arguments
+ hpstat_args = [config.hpstat_path, 'turnbull', '-', '--output', 'json']
- xpoints = [i.left*(1-step_loc) + i.right*step_loc for i in turnbull_intervals]
- ypoints = 1 - np.cumsum(sf_probs)
+ # Export data to CSV
+ csv_buf = io.StringIO()
+ pd.DataFrame({'LeftTime': time_left, 'RightTime': time_right}).to_csv(csv_buf, index=False)
+ csv_str = csv_buf.getvalue()
+
+ # Run hpstat binary
+ proc = subprocess.run(hpstat_args, input=csv_str, stdout=subprocess.PIPE, stderr=None, encoding='utf-8', check=True)
+ raw_result = json.loads(proc.stdout)
+
+ survival_prob = np.array(raw_result['survival_prob'])
+
+ from IPython.display import clear_output
+ clear_output(wait=True)
+
+ xpoints = [i[0]*(1-step_loc) + i[1]*step_loc for i in raw_result['failure_intervals'] if i[1]]
+ ypoints = survival_prob
# Draw straight lines
# np.concatenate(...) to force starting drawing from time 0, survival 100%
@@ -226,9 +250,27 @@ def calc_survfunc_turnbull(time_left, time_right, step_loc=0.5, transform_x=None
if transform_y:
ypoints = transform_y(ypoints)
- return xpoints, ypoints
+ if ci:
+ zstar = -stats.norm.ppf(config.alpha/2)
+ survival_prob_se = np.array(raw_result['survival_prob_se'])
+
+ # Get confidence intervals
+ ci0 = survival_prob - zstar * survival_prob_se
+ ci1 = survival_prob + zstar * survival_prob_se
+
+ # Plot confidence intervals
+ ypoints0 = np.concatenate([[1], ci0]).repeat(2)[:-1]
+ ypoints1 = np.concatenate([[1], ci1]).repeat(2)[:-1]
+
+ if transform_y:
+ ypoints0 = transform_y(ypoints0)
+ ypoints1 = transform_y(ypoints1)
+
+ return xpoints, ypoints, ypoints0, ypoints1
+
+ return xpoints, ypoints, None, None
-def survtime_to_numeric(df, time):
+def survtime_to_numeric(df, time, time2=None):
"""
Convert pandas timedelta dtype to float64, auto-detecting the best time unit to display
@@ -236,6 +278,8 @@ def survtime_to_numeric(df, time):
:type df: DataFrame
:param time: Column to check for pandas timedelta dtype
:type df: DataFrame
+ :param time: Second column, if any, to check for pandas timedelta dtype
+ :type df: DataFrame
:return: (*df*, *time_units*)
@@ -243,28 +287,42 @@ def survtime_to_numeric(df, time):
* **time_units** (*str*) – Human-readable description of the time unit, or *None* if not converted
"""
+ max_time = None
+
if df[time].dtype == ' 365.24*24*60*60:
- df[time] = df[time] / (365.24*24*60*60)
+ if max_time > 365.24*24*60*60:
+ time_divider = 365.24*24*60*60
time_units = 'years'
- elif df[time].max() > 7*24*60*60 / 12:
- df[time] = df[time] / (7*24*60*60)
+ elif max_time > 7*24*60*60 / 12:
+ time_divider = 7*24*60*60
time_units = 'weeks'
- elif df[time].max() > 24*60*60:
- df[time] = df[time] / (24*60*60)
+ elif max_time > 24*60*60:
+ time_divider = 24*60*60
time_units = 'days'
- elif df[time].max() > 60*60:
- df[time] = df[time] / (60*60)
+ elif max_time > 60*60:
+ time_divider = 60*60
time_units = 'hours'
- elif df[time].max() > 60:
- df[time] = df[time] / 60
+ elif max_time > 60:
+ time_divider = 60
time_units = 'minutes'
else:
+ time_divider = 1
time_units = 'seconds'
+ df[time] /= time_divider
+
+ if time2:
+ df[time2] /= time_divider
+
return df, time_units
else:
return df, None