155 lines
4.5 KiB
Python
155 lines
4.5 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, 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 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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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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else:
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time_units = None
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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(ax, subset[time], subset[status], ci)
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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(ax, df[time], df[status], ci)
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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_ylim(0, 1)
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ax.legend()
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return fig, ax
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def plot_survfunc(ax, time, status, ci):
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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 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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ax.fill_between(xpoints, ypoints0, ypoints1, alpha=0.3, label='_')
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return handle
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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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