274 lines
8.5 KiB
Python
274 lines
8.5 KiB
Python
# scipy-yli: Helpful SciPy utilities and recipes
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# Copyright © 2022–2023 Lee Yingtong Li (RunasSudo)
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Affero General Public License for more details.
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#
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# You should have received a copy of the GNU Affero General Public License
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# along with this program. If not, see <https://www.gnu.org/licenses/>.
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from scipy import stats
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import statsmodels.api as sm
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from .config import config
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from .sig_tests import ChiSquaredResult
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from .utils import check_nan
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def kaplanmeier(df, time, status, by=None, *, ci=True, transform_x=None, transform_y=None, nan_policy='warn'):
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"""
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Generate a Kaplan–Meier plot
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Uses the Python *matplotlib* library.
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:param df: Data to generate plot for
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:type df: DataFrame
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:param time: Column in *df* for the time to event (numeric or timedelta)
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:type time: str
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:param status: Column in *df* for the status variable (True/False or 1/0)
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:type status: str
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:param by: Column in *df* to stratify by (categorical)
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:type by: str
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:param ci: Whether to plot confidence intervals around the survival function
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:type ci: bool
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:param transform_x: Function to transform x axis by
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:type transform_x: callable
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:param transform_y: Function to transform y axis by
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:type transform_y: callable
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:param nan_policy: How to handle *nan* values (see :ref:`nan-handling`)
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:type nan_policy: str
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:rtype: (Figure, Axes)
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"""
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import matplotlib.pyplot as plt
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# Check for/clean NaNs
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if by:
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df = check_nan(df[[time, status, by]], nan_policy)
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else:
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df = check_nan(df[[time, status]], nan_policy)
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# Covert timedelta to numeric
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df, time_units = survtime_to_numeric(df, time)
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fig, ax = plt.subplots()
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if by is not None:
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# Group by independent variable
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groups = df.groupby(by)
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for group in groups.groups:
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subset = groups.get_group(group)
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handle = plot_survfunc_kaplanmeier(ax, subset[time], subset[status], ci, transform_x, transform_y)
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handle.set_label('{} = {}'.format(by, group))
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else:
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# No grouping
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plot_survfunc_kaplanmeier(ax, df[time], df[status], ci, transform_x, transform_y)
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if time_units:
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ax.set_xlabel('{} ({})'.format(time, time_units))
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else:
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ax.set_xlabel(time)
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ax.set_ylabel('Survival probability ({:.0%} CI)'.format(1-config.alpha) if ci else 'Survival probability')
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ax.set_xlim(left=0)
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ax.set_ylim(0, 1)
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ax.legend()
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return fig, ax
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def plot_survfunc_kaplanmeier(ax, time, status, ci, transform_x=None, transform_y=None):
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# Estimate the survival function
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sf = sm.SurvfuncRight(time, status)
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# Draw straight lines
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xpoints = sf.surv_times.repeat(2)[1:]
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ypoints = sf.surv_prob.repeat(2)[:-1]
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handle = ax.plot(xpoints, ypoints)[0]
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if transform_x:
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xpoints = transform_x(xpoints)
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if transform_y:
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ypoints = transform_y(ypoints)
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if ci:
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zstar = -stats.norm.ppf(config.alpha/2)
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# Get confidence intervals
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ci0 = sf.surv_prob - zstar * sf.surv_prob_se
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ci1 = sf.surv_prob + zstar * sf.surv_prob_se
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# Plot confidence intervals
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ypoints0 = ci0.repeat(2)[:-1]
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ypoints1 = ci1.repeat(2)[:-1]
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if transform_y:
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ypoints0 = transform_y(ypoints0)
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ypoints1 = transform_y(ypoints1)
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ax.fill_between(xpoints, ypoints0, ypoints1, alpha=0.3, label='_')
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return handle
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def turnbull(df, time_left, time_right, by=None, *, transform_x=None, transform_y=None, nan_policy='warn'):
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"""
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Generate a Turnbull estimator plot, which extends the Kaplan–Meier estimator to interval-censored observations
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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.
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For ease of interpretation, the survival function is drawn as a step function at the midpoint of the estimate on each interval.
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Uses the Python *lifelines* and *matplotlib* libraries.
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:param df: Data to generate plot for
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:type df: DataFrame
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:param time_left: Column in *df* for the time to event, left interval endpoint (numeric)
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:type time_left: str
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:param time_right: Column in *df* for the time to event, right interval endpoint (numeric)
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:type time_right: str
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:param by: Column in *df* to stratify by (categorical)
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:type by: str
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:param transform_x: Function to transform x axis by
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:type transform_x: callable
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:param transform_y: Function to transform y axis by
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:type transform_y: callable
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:param nan_policy: How to handle *nan* values (see :ref:`nan-handling`)
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:type nan_policy: str
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:rtype: (Figure, Axes)
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"""
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import matplotlib.pyplot as plt
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# Check for/clean NaNs
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if by:
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df = check_nan(df[[time_left, time_right, by]], nan_policy)
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else:
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df = check_nan(df[[time_left, time_right]], nan_policy)
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fig, ax = plt.subplots()
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if by is not None:
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# Group by independent variable
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groups = df.groupby(by)
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for group in groups.groups:
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subset = groups.get_group(group)
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handle = plot_survfunc_turnbull(ax, subset[time_left], subset[time_right], transform_x, transform_y)
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handle.set_label('{} = {}'.format(by, group))
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else:
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# No grouping
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plot_survfunc_turnbull(ax, df[time_left], df[time_right], transform_x, transform_y)
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ax.set_xlabel('Analysis time')
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ax.set_ylabel('Survival probability')
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ax.set_xlim(left=0)
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ax.set_ylim(0, 1)
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ax.legend()
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return fig, ax
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def plot_survfunc_turnbull(ax, time_left, time_right, transform_x=None, transform_y=None):
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import lifelines
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EPSILON = 1e-10
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# TODO: Support left == right => failure was exactly observed
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followup_left = time_left + EPSILON # Add epsilon to make interval half-open
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followup_right = time_right
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# Estimate the survival function
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sf = lifelines.KaplanMeierFitter().fit_interval_censoring(followup_left, followup_right)
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# Draw straight lines
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xpoints = sf.survival_function_.index.to_numpy().repeat(2)[:-1]
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med = (sf.survival_function_['NPMLE_estimate_upper'] + sf.survival_function_['NPMLE_estimate_lower']) / 2
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ypoints = med.to_numpy().repeat(2)[1:]
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if transform_x:
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xpoints = transform_x(xpoints)
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if transform_y:
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ypoints = transform_y(ypoints)
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handle = ax.plot(xpoints, ypoints)[0]
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return handle
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def survtime_to_numeric(df, time):
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"""
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Convert pandas timedelta dtype to float64, auto-detecting the best time unit to display
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:param df: Data to check for pandas timedelta dtype
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:type df: DataFrame
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:param time: Column to check for pandas timedelta dtype
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:type df: DataFrame
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:return: (*df*, *time_units*)
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* **df** (*DataFrame*) – Data with pandas timedelta dtypes converted, which is *not* copied
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* **time_units** (*str*) – Human-readable description of the time unit, or *None* if not converted
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"""
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if df[time].dtype == '<m8[ns]':
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df[time] = df[time].dt.total_seconds()
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# Auto-detect best time units
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if df[time].max() > 365.24*24*60*60:
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df[time] = df[time] / (365.24*24*60*60)
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time_units = 'years'
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elif df[time].max() > 7*24*60*60 / 12:
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df[time] = df[time] / (7*24*60*60)
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time_units = 'weeks'
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elif df[time].max() > 24*60*60:
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df[time] = df[time] / (24*60*60)
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time_units = 'days'
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elif df[time].max() > 60*60:
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df[time] = df[time] / (60*60)
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time_units = 'hours'
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elif df[time].max() > 60:
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df[time] = df[time] / 60
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time_units = 'minutes'
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else:
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time_units = 'seconds'
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return df, time_units
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else:
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return df, None
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def logrank(df, time, status, by, nan_policy='warn'):
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"""
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Perform the log-rank test for equality of survival functions
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:param df: Data to perform the test on
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:type df: DataFrame
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:param time: Column in *df* for the time to event (numeric or timedelta)
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:type time: str
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:param status: Column in *df* for the status variable (True/False or 1/0)
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:type status: str
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:param by: Column in *df* to stratify by (categorical)
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:type by: str
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:param nan_policy: How to handle *nan* values (see :ref:`nan-handling`)
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:type nan_policy: str
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:rtype: :class:`yli.sig_tests.ChiSquaredResult`
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"""
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# TODO: Example
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# Check for/clean NaNs
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df = check_nan(df[[time, status, by]], nan_policy)
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if df[time].dtype == '<m8[ns]':
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df[time] = df[time].dt.total_seconds()
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statistic, pvalue = sm.duration.survdiff(df[time], df[status], df[by])
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return ChiSquaredResult(statistic=statistic, dof=1, pvalue=pvalue)
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