RunasSudo/scipy-yli
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Fix up yli.turnbull

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Draw step function at configurable point on each Turnbull interval (previous documentation did not correctly describe the behaviour of the function)
Draw survival curve from time 0, survival 100%
This commit is contained in:
RunasSudo 2023-04-22 00:43:01 +10:00
parent 8899d1c968
commit 8238383edb
Signed by: RunasSudo
GPG Key ID: 7234E476BF21C61A
1 changed files with 24 additions and 13 deletions
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37
yli/survival.py
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@ -118,13 +118,13 @@ def plot_survfunc_kaplanmeier(ax, time, status, ci, transform_x=None, transform_
return handle
def turnbull(df, time_left, time_right, by=None, *, transform_x=None, transform_y=None, nan_policy='warn'):
def turnbull(df, time_left, time_right, by=None, *, step_loc=0.5, transform_x=None, transform_y=None, nan_policy='warn'):
"""
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.
For ease of interpretation, the survival function is drawn as a step function at the midpoint of the estimate on each interval.
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.
@ -136,6 +136,8 @@ def turnbull(df, time_left, time_right, by=None, *, transform_x=None, transform_
: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)
:type step_loc: float
:param transform_x: Function to transform x axis by
:type transform_x: callable
:param transform_y: Function to transform y axis by
@ -162,11 +164,11 @@ def turnbull(df, time_left, time_right, by=None, *, transform_x=None, transform_
for group in groups.groups:
subset = groups.get_group(group)
handle = plot_survfunc_turnbull(ax, subset[time_left], subset[time_right], transform_x, transform_y)
handle = plot_survfunc_turnbull(ax, subset[time_left], subset[time_right], 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], transform_x, transform_y)
plot_survfunc_turnbull(ax, df[time_left], df[time_right], step_loc, transform_x, transform_y)
ax.set_xlabel('Analysis time')
ax.set_ylabel('Survival probability')
@ -176,8 +178,13 @@ def turnbull(df, time_left, time_right, by=None, *, transform_x=None, transform_
return fig, ax
def plot_survfunc_turnbull(ax, time_left, time_right, transform_x=None, transform_y=None):
import lifelines
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)
handle = ax.plot(xpoints, ypoints)[0]
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
@ -187,21 +194,25 @@ def plot_survfunc_turnbull(ax, time_left, time_right, transform_x=None, transfor
followup_right = time_right
# Estimate the survival function
sf = lifelines.KaplanMeierFitter().fit_interval_censoring(followup_left, followup_right)
#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)
xpoints = [i.left*(1-step_loc) + i.right*step_loc for i in turnbull_intervals]
ypoints = 1 - np.cumsum(sf_probs)
# Draw straight lines
xpoints = sf.survival_function_.index.to_numpy().repeat(2)[:-1]
med = (sf.survival_function_['NPMLE_estimate_upper'] + sf.survival_function_['NPMLE_estimate_lower']) / 2
ypoints = med.to_numpy().repeat(2)[1:]
# np.concatenate(...) to force starting drawing from time 0, survival 100%
xpoints = np.concatenate([[0], xpoints]).repeat(2)[1:]
ypoints = np.concatenate([[1], ypoints]).repeat(2)[:-1]
if transform_x:
xpoints = transform_x(xpoints)
if transform_y:
ypoints = transform_y(ypoints)
handle = ax.plot(xpoints, ypoints)[0]
return handle
return xpoints, ypoints
def survtime_to_numeric(df, time):
"""