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Greenwood confidence interval #400

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Greenwood confidence interval #400

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cc4e252
Greenwood confidence intervals for KM estimator
TristanFauvel Aug 24, 2023
eea0bd1
Tests for the Greenwood CI.
TristanFauvel Aug 24, 2023
5fb8bb9
Debug test_nonparametric
TristanFauvel Aug 28, 2023
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31 changes: 24 additions & 7 deletions sksurv/nonparametric.py
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Original file line number Diff line number Diff line change
Expand Up @@ -189,16 +189,29 @@ def _ci_logmlog(prob_survival, sigma_t, z):
return ci


def _ci_greenwood(prob_survival, sigma_t, z):
"""Compute the pointwise Greenwood confidence intervals
See https://www.math.wustl.edu/~sawyer/handouts/greenwood.pdf.
"""
var_survival_prob = prob_survival**2 * sigma_t**2
ci = prob_survival + np.array([[-1], [1]]) * z * np.sqrt(var_survival_prob)
return ci


def _km_ci_estimator(prob_survival, ratio_var, conf_level, conf_type):
if conf_type not in {"log-log"}:
raise ValueError(f"conf_type must be None or a str among {{'log-log'}}, but was {conf_type!r}")
if conf_type not in {"log-log", "greenwood"}:
raise ValueError(f"conf_type must be None or a str among {{'log-log', 'greenwood'}}, but was {conf_type!r}")

if not isinstance(conf_level, numbers.Real) or not np.isfinite(conf_level) or conf_level <= 0 or conf_level >= 1.0:
raise ValueError(f"conf_level must be a float in the range (0.0, 1.0), but was {conf_level!r}")

z = stats.norm.isf((1.0 - conf_level) / 2.0)
sigma = np.sqrt(np.cumsum(ratio_var))
ci = _ci_logmlog(prob_survival, sigma, z)
sigma_t = np.sqrt(np.cumsum(ratio_var))

if conf_type == "log-log":
ci = _ci_logmlog(prob_survival, sigma_t, z)
elif conf_type == "greenwood":
ci = _ci_greenwood(prob_survival, sigma_t, z)
return ci


Expand Down Expand Up @@ -241,11 +254,13 @@ def kaplan_meier_estimator(
conf_level : float, optional, default: 0.95
The level for a two-sided confidence interval on the survival curves.

conf_type : None or {'log-log'}, optional, default: 'log-log'.
conf_type : None or {'log-log', 'greenwood'}, optional, default: 'log-log'.
The type of confidence intervals to estimate.
If `None`, no confidence intervals are estimated.
If "log-log", estimate confidence intervals using
the log hazard or :math:`log(-log(S(t)))` as described in [2]_.
If "greenwood", estimate confidence intervals using
the Greenwood variance formula.

Returns
-------
Expand Down Expand Up @@ -421,11 +436,13 @@ class SurvivalFunctionEstimator(BaseEstimator):
conf_level : float, optional, default: 0.95
The level for a two-sided confidence interval on the survival curves.

conf_type : None or {'log-log'}, optional, default: 'log-log'.
conf_type : None or {'log-log', 'greenwood'}, optional, default: 'log-log'.
The type of confidence intervals to estimate.
If `None`, no confidence intervals are estimated.
If "log-log", estimate confidence intervals using
the log hazard or :math:`log(-log(S(t)))`.
If "greenwood", estimate confidence intervals using
the Greenwood variance formula.

See also
--------
Expand All @@ -435,7 +452,7 @@ class SurvivalFunctionEstimator(BaseEstimator):

_parameter_constraints = {
"conf_level": [Interval(numbers.Real, 0.0, 1.0, closed="neither")],
"conf_type": [None, StrOptions({"log-log"})],
"conf_type": [None, StrOptions({"log-log", "greenwood"})],
}

def __init__(self, conf_level=0.95, conf_type=None):
Expand Down
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