Use hpstat for yli.turnbull to enable computation of confidence intervals

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

yli/survival.py

@@ -15,9 +15,14 @@
 #   along with this program.  If not, see <https://www.gnu.org/licenses/>.
 
 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):
+def plot_survfunc_turnbull(ax, time_left, time_right, ci, 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)
+	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):
+def calc_survfunc_turnbull(time_left, time_right, ci, 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
-	
 	# 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.
+	# Prepare arguments
-	sf_probs, turnbull_intervals = npmle(followup_left, followup_right)
+	# 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]
+	# Export data to CSV
-	ypoints = 1 - np.cumsum(sf_probs)
+	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 == '<m8[ns]':
 		df[time] = df[time].dt.total_seconds()
-		
+		max_time = df[time].max()
+	
+	if df[time2].dtype == '<m8[ns]':
+		df[time2] = df[time2].dt.total_seconds()
+		max_time = max(max_time or 0, df[time2].max())
+	
+	if max_time is not None:
 		# Auto-detect best time units
-		if df[time].max() > 365.24*24*60*60:
+		if max_time > 365.24*24*60*60:
-			df[time] = df[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:
+		elif max_time > 7*24*60*60 / 12:
-			df[time] = df[time] / (7*24*60*60)
+			time_divider = 7*24*60*60
 			time_units = 'weeks'
-		elif df[time].max() > 24*60*60:
+		elif max_time > 24*60*60:
-			df[time] = df[time] / (24*60*60)
+			time_divider = 24*60*60
 			time_units = 'days'
-		elif df[time].max() > 60*60:
+		elif max_time > 60*60:
-			df[time] = df[time] / (60*60)
+			time_divider = 60*60
 			time_units = 'hours'
-		elif df[time].max() > 60:
+		elif max_time > 60:
-			df[time] = df[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