Methods for Survival and Duration Analysis#
statsmodels.duration implements several standard methods for
working with censored data. These methods are most commonly used when
the data consist of durations between an origin time point and the
time at which some event of interest occurred. A typical example is a
medical study in which the origin is the time at which a subject is
diagnosed with some condition, and the event of interest is death (or
disease progression, recovery, etc.).
Currently only right-censoring is handled. Right censoring occurs when we know that an event occurred after a given time t, but we do not know the exact event time.
Survival function estimation and inference#
The statsmodels.api.SurvfuncRight class can be used to
estimate a survival function using data that may be right censored.
SurvfuncRight implements several inference procedures including
confidence intervals for survival distribution quantiles, pointwise
and simultaneous confidence bands for the survival function, and
plotting procedures. The duration.survdiff function provides
testing procedures for comparing survival distributions.
Here we create a SurvfuncRight object using data from the
flchain study, which is available through the R datasets repository.
We fit the survival distribution only for the female subjects.
In [1]: import statsmodels.api as sm
In [2]: data = sm.datasets.get_rdataset("flchain", "survival", cache=True).data
---------------------------------------------------------------------------
ConnectionRefusedError Traceback (most recent call last)
File /usr/lib/python3.14/urllib/request.py:1333, in AbstractHTTPHandler.do_open(self, http_class, req, **http_conn_args)
1332 try:
-> 1333 h.request(req.get_method(), req.selector, req.data, headers,
1334 encode_chunked=req.has_header('Transfer-encoding'))
1335 except OSError as err: # timeout error
File /usr/lib/python3.14/http/client.py:1386, in HTTPConnection.request(self, method, url, body, headers, encode_chunked)
1385 """Send a complete request to the server."""
-> 1386 self._send_request(method, url, body, headers, encode_chunked)
File /usr/lib/python3.14/http/client.py:1432, in HTTPConnection._send_request(self, method, url, body, headers, encode_chunked)
1431 body = _encode(body, 'body')
-> 1432 self.endheaders(body, encode_chunked=encode_chunked)
File /usr/lib/python3.14/http/client.py:1381, in HTTPConnection.endheaders(self, message_body, encode_chunked)
1380 raise CannotSendHeader()
-> 1381 self._send_output(message_body, encode_chunked=encode_chunked)
File /usr/lib/python3.14/http/client.py:1141, in HTTPConnection._send_output(self, message_body, encode_chunked)
1140 del self._buffer[:]
-> 1141 self.send(msg)
1143 if message_body is not None:
1144
1145 # create a consistent interface to message_body
File /usr/lib/python3.14/http/client.py:1085, in HTTPConnection.send(self, data)
1084 if self.auto_open:
-> 1085 self.connect()
1086 else:
File /usr/lib/python3.14/http/client.py:1520, in HTTPSConnection.connect(self)
1518 "Connect to a host on a given (SSL) port."
-> 1520 super().connect()
1522 if self._tunnel_host:
File /usr/lib/python3.14/http/client.py:1051, in HTTPConnection.connect(self)
1050 sys.audit("http.client.connect", self, self.host, self.port)
-> 1051 self.sock = self._create_connection(
1052 (self.host,self.port), self.timeout, self.source_address)
1053 # Might fail in OSs that don't implement TCP_NODELAY
File /usr/lib/python3.14/socket.py:874, in create_connection(address, timeout, source_address, all_errors)
873 if not all_errors:
--> 874 raise exceptions[0]
875 raise ExceptionGroup("create_connection failed", exceptions)
File /usr/lib/python3.14/socket.py:859, in create_connection(address, timeout, source_address, all_errors)
858 sock.bind(source_address)
--> 859 sock.connect(sa)
860 # Break explicitly a reference cycle
ConnectionRefusedError: [Errno 111] Connection refused
During handling of the above exception, another exception occurred:
URLError Traceback (most recent call last)
Cell In[2], line 1
----> 1 data = sm.datasets.get_rdataset("flchain", "survival", cache=True).data
File /usr/lib/python3/dist-packages/statsmodels/datasets/utils.py:308, in get_rdataset(dataname, package, cache)
305 docs_base_url = ("https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/"
306 "master/doc/"+package+"/rst/")
307 cache = _get_cache(cache)
--> 308 data, from_cache = _get_data(data_base_url, dataname, cache)
309 data = read_csv(data, index_col=0)
310 data = _maybe_reset_index(data)
File /usr/lib/python3/dist-packages/statsmodels/datasets/utils.py:237, in _get_data(base_url, dataname, cache, extension)
235 url = base_url + (dataname + ".%s") % extension
236 try:
--> 237 data, from_cache = _urlopen_cached(url, cache)
238 except HTTPError as err:
239 if "404" in str(err):
File /usr/lib/python3/dist-packages/statsmodels/datasets/utils.py:228, in _urlopen_cached(url, cache)
226 # not using the cache or did not find it in cache
227 if not from_cache:
--> 228 data = urlopen(url, timeout=3).read()
229 if cache is not None: # then put it in the cache
230 _cache_it(data, cache_path)
File /usr/lib/python3.14/urllib/request.py:187, in urlopen(url, data, timeout, context)
185 else:
186 opener = _opener
--> 187 return opener.open(url, data, timeout)
File /usr/lib/python3.14/urllib/request.py:487, in OpenerDirector.open(self, fullurl, data, timeout)
484 req = meth(req)
486 sys.audit('urllib.Request', req.full_url, req.data, req.headers, req.get_method())
--> 487 response = self._open(req, data)
489 # post-process response
490 meth_name = protocol+"_response"
File /usr/lib/python3.14/urllib/request.py:504, in OpenerDirector._open(self, req, data)
501 return result
503 protocol = req.type
--> 504 result = self._call_chain(self.handle_open, protocol, protocol +
505 '_open', req)
506 if result:
507 return result
File /usr/lib/python3.14/urllib/request.py:464, in OpenerDirector._call_chain(self, chain, kind, meth_name, *args)
462 for handler in handlers:
463 func = getattr(handler, meth_name)
--> 464 result = func(*args)
465 if result is not None:
466 return result
File /usr/lib/python3.14/urllib/request.py:1381, in HTTPSHandler.https_open(self, req)
1380 def https_open(self, req):
-> 1381 return self.do_open(http.client.HTTPSConnection, req,
1382 context=self._context)
File /usr/lib/python3.14/urllib/request.py:1336, in AbstractHTTPHandler.do_open(self, http_class, req, **http_conn_args)
1333 h.request(req.get_method(), req.selector, req.data, headers,
1334 encode_chunked=req.has_header('Transfer-encoding'))
1335 except OSError as err: # timeout error
-> 1336 raise URLError(err)
1337 r = h.getresponse()
1338 except:
URLError: <urlopen error [Errno 111] Connection refused>
In [3]: df = data.loc[data.sex == "F", :]
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[3], line 1
----> 1 df = data.loc[data.sex == "F", :]
AttributeError: 'Dataset' object has no attribute 'loc'
In [4]: sf = sm.SurvfuncRight(df["futime"], df["death"])
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
File /usr/lib/python3/dist-packages/pandas/core/indexes/base.py:3812, in Index.get_loc(self, key)
3811 try:
-> 3812 return self._engine.get_loc(casted_key)
3813 except KeyError as err:
File pandas/_libs/index.pyx:167, in pandas._libs.index.IndexEngine.get_loc()
File pandas/_libs/index.pyx:196, in pandas._libs.index.IndexEngine.get_loc()
File pandas/_libs/hashtable_class_helper.pxi:7088, in pandas._libs.hashtable.PyObjectHashTable.get_item()
File pandas/_libs/hashtable_class_helper.pxi:7096, in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: 'futime'
The above exception was the direct cause of the following exception:
KeyError Traceback (most recent call last)
Cell In[4], line 1
----> 1 sf = sm.SurvfuncRight(df["futime"], df["death"])
File /usr/lib/python3/dist-packages/pandas/core/frame.py:4113, in DataFrame.__getitem__(self, key)
4111 if self.columns.nlevels > 1:
4112 return self._getitem_multilevel(key)
-> 4113 indexer = self.columns.get_loc(key)
4114 if is_integer(indexer):
4115 indexer = [indexer]
File /usr/lib/python3/dist-packages/pandas/core/indexes/base.py:3819, in Index.get_loc(self, key)
3814 if isinstance(casted_key, slice) or (
3815 isinstance(casted_key, abc.Iterable)
3816 and any(isinstance(x, slice) for x in casted_key)
3817 ):
3818 raise InvalidIndexError(key)
-> 3819 raise KeyError(key) from err
3820 except TypeError:
3821 # If we have a listlike key, _check_indexing_error will raise
3822 # InvalidIndexError. Otherwise we fall through and re-raise
3823 # the TypeError.
3824 self._check_indexing_error(key)
KeyError: 'futime'
The main features of the fitted survival distribution can be seen by
calling the summary method:
In [5]: sf.summary().head()
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[5], line 1
----> 1 sf.summary().head()
NameError: name 'sf' is not defined
We can obtain point estimates and confidence intervals for quantiles of the survival distribution. Since only around 30% of the subjects died during this study, we can only estimate quantiles below the 0.3 probability point:
In [6]: sf.quantile(0.25)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[6], line 1
----> 1 sf.quantile(0.25)
NameError: name 'sf' is not defined
In [7]: sf.quantile_ci(0.25)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[7], line 1
----> 1 sf.quantile_ci(0.25)
NameError: name 'sf' is not defined
To plot a single survival function, call the plot method:
In [8]: sf.plot()
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[8], line 1
----> 1 sf.plot()
NameError: name 'sf' is not defined
Since this is a large dataset with a lot of censoring, we may wish to not plot the censoring symbols:
In [9]: fig = sf.plot()
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[9], line 1
----> 1 fig = sf.plot()
NameError: name 'sf' is not defined
In [10]: ax = fig.get_axes()[0]
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
Cell In[10], line 1
----> 1 ax = fig.get_axes()[0]
IndexError: list index out of range
In [11]: pt = ax.get_lines()[1]
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[11], line 1
----> 1 pt = ax.get_lines()[1]
NameError: name 'ax' is not defined
In [12]: pt.set_visible(False)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[12], line 1
----> 1 pt.set_visible(False)
NameError: name 'pt' is not defined
We can also add a 95% simultaneous confidence band to the plot. Typically these bands only plotted for central part of the distribution.
In [13]: fig = sf.plot()
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[13], line 1
----> 1 fig = sf.plot()
NameError: name 'sf' is not defined
In [14]: lcb, ucb = sf.simultaneous_cb()
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[14], line 1
----> 1 lcb, ucb = sf.simultaneous_cb()
NameError: name 'sf' is not defined
In [15]: ax = fig.get_axes()[0]
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
Cell In[15], line 1
----> 1 ax = fig.get_axes()[0]
IndexError: list index out of range
In [16]: ax.fill_between(sf.surv_times, lcb, ucb, color='lightgrey')
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[16], line 1
----> 1 ax.fill_between(sf.surv_times, lcb, ucb, color='lightgrey')
NameError: name 'ax' is not defined
In [17]: ax.set_xlim(365, 365*10)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[17], line 1
----> 1 ax.set_xlim(365, 365*10)
NameError: name 'ax' is not defined
In [18]: ax.set_ylim(0.7, 1)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[18], line 1
----> 1 ax.set_ylim(0.7, 1)
NameError: name 'ax' is not defined
In [19]: ax.set_ylabel("Proportion alive")
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[19], line 1
----> 1 ax.set_ylabel("Proportion alive")
NameError: name 'ax' is not defined
In [20]: ax.set_xlabel("Days since enrollment")
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[20], line 1
----> 1 ax.set_xlabel("Days since enrollment")
NameError: name 'ax' is not defined
Here we plot survival functions for two groups (females and males) on the same axes:
In [21]: import matplotlib.pyplot as plt
In [22]: gb = data.groupby("sex")
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[22], line 1
----> 1 gb = data.groupby("sex")
AttributeError: 'Dataset' object has no attribute 'groupby'
In [23]: ax = plt.axes()
In [24]: sexes = []
In [25]: for g in gb:
....: sexes.append(g[0])
....: sf = sm.SurvfuncRight(g[1]["futime"], g[1]["death"])
....: sf.plot(ax)
....:
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[25], line 1
----> 1 for g in gb:
2 sexes.append(g[0])
3 sf = sm.SurvfuncRight(g[1]["futime"], g[1]["death"])
NameError: name 'gb' is not defined
In [26]: li = ax.get_lines()
In [27]: li[1].set_visible(False)
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
Cell In[27], line 1
----> 1 li[1].set_visible(False)
IndexError: list index out of range
In [28]: li[3].set_visible(False)
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
Cell In[28], line 1
----> 1 li[3].set_visible(False)
IndexError: list index out of range
In [29]: plt.figlegend((li[0], li[2]), sexes, loc="center right")
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
Cell In[29], line 1
----> 1 plt.figlegend((li[0], li[2]), sexes, loc="center right")
IndexError: list index out of range
In [30]: plt.ylim(0.6, 1)
Out[30]: (0.6, 1.0)
In [31]: ax.set_ylabel("Proportion alive")
Out[31]: Text(0, 0.5, 'Proportion alive')
In [32]: ax.set_xlabel("Days since enrollment")
Out[32]: Text(0.5, 0, 'Days since enrollment')
We can formally compare two survival distributions with survdiff,
which implements several standard nonparametric procedures. The
default procedure is the logrank test:
In [33]: stat, pv = sm.duration.survdiff(data.futime, data.death, data.sex)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[33], line 1
----> 1 stat, pv = sm.duration.survdiff(data.futime, data.death, data.sex)
AttributeError: 'Dataset' object has no attribute 'futime'
Here are some of the other testing procedures implemented by survdiff:
# Fleming-Harrington with p=1, i.e. weight by pooled survival time
In [34]: stat, pv = sm.duration.survdiff(data.futime, data.death, data.sex, weight_type='fh', fh_p=1)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[34], line 1
----> 1 stat, pv = sm.duration.survdiff(data.futime, data.death, data.sex, weight_type='fh', fh_p=1)
AttributeError: 'Dataset' object has no attribute 'futime'
# Gehan-Breslow, weight by number at risk
In [35]: stat, pv = sm.duration.survdiff(data.futime, data.death, data.sex, weight_type='gb')
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[35], line 1
----> 1 stat, pv = sm.duration.survdiff(data.futime, data.death, data.sex, weight_type='gb')
AttributeError: 'Dataset' object has no attribute 'futime'
# Tarone-Ware, weight by the square root of the number at risk
In [36]: stat, pv = sm.duration.survdiff(data.futime, data.death, data.sex, weight_type='tw')
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[36], line 1
----> 1 stat, pv = sm.duration.survdiff(data.futime, data.death, data.sex, weight_type='tw')
AttributeError: 'Dataset' object has no attribute 'futime'
Regression methods#
Proportional hazard regression models (“Cox models”) are a regression technique for censored data. They allow variation in the time to an event to be explained in terms of covariates, similar to what is done in a linear or generalized linear regression model. These models express the covariate effects in terms of “hazard ratios”, meaning the the hazard (instantaneous event rate) is multiplied by a given factor depending on the value of the covariates.
In [37]: import statsmodels.api as sm
In [38]: import statsmodels.formula.api as smf
In [39]: data = sm.datasets.get_rdataset("flchain", "survival", cache=True).data
---------------------------------------------------------------------------
ConnectionRefusedError Traceback (most recent call last)
File /usr/lib/python3.14/urllib/request.py:1333, in AbstractHTTPHandler.do_open(self, http_class, req, **http_conn_args)
1332 try:
-> 1333 h.request(req.get_method(), req.selector, req.data, headers,
1334 encode_chunked=req.has_header('Transfer-encoding'))
1335 except OSError as err: # timeout error
File /usr/lib/python3.14/http/client.py:1386, in HTTPConnection.request(self, method, url, body, headers, encode_chunked)
1385 """Send a complete request to the server."""
-> 1386 self._send_request(method, url, body, headers, encode_chunked)
File /usr/lib/python3.14/http/client.py:1432, in HTTPConnection._send_request(self, method, url, body, headers, encode_chunked)
1431 body = _encode(body, 'body')
-> 1432 self.endheaders(body, encode_chunked=encode_chunked)
File /usr/lib/python3.14/http/client.py:1381, in HTTPConnection.endheaders(self, message_body, encode_chunked)
1380 raise CannotSendHeader()
-> 1381 self._send_output(message_body, encode_chunked=encode_chunked)
File /usr/lib/python3.14/http/client.py:1141, in HTTPConnection._send_output(self, message_body, encode_chunked)
1140 del self._buffer[:]
-> 1141 self.send(msg)
1143 if message_body is not None:
1144
1145 # create a consistent interface to message_body
File /usr/lib/python3.14/http/client.py:1085, in HTTPConnection.send(self, data)
1084 if self.auto_open:
-> 1085 self.connect()
1086 else:
File /usr/lib/python3.14/http/client.py:1520, in HTTPSConnection.connect(self)
1518 "Connect to a host on a given (SSL) port."
-> 1520 super().connect()
1522 if self._tunnel_host:
File /usr/lib/python3.14/http/client.py:1051, in HTTPConnection.connect(self)
1050 sys.audit("http.client.connect", self, self.host, self.port)
-> 1051 self.sock = self._create_connection(
1052 (self.host,self.port), self.timeout, self.source_address)
1053 # Might fail in OSs that don't implement TCP_NODELAY
File /usr/lib/python3.14/socket.py:874, in create_connection(address, timeout, source_address, all_errors)
873 if not all_errors:
--> 874 raise exceptions[0]
875 raise ExceptionGroup("create_connection failed", exceptions)
File /usr/lib/python3.14/socket.py:859, in create_connection(address, timeout, source_address, all_errors)
858 sock.bind(source_address)
--> 859 sock.connect(sa)
860 # Break explicitly a reference cycle
ConnectionRefusedError: [Errno 111] Connection refused
During handling of the above exception, another exception occurred:
URLError Traceback (most recent call last)
Cell In[39], line 1
----> 1 data = sm.datasets.get_rdataset("flchain", "survival", cache=True).data
File /usr/lib/python3/dist-packages/statsmodels/datasets/utils.py:308, in get_rdataset(dataname, package, cache)
305 docs_base_url = ("https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/"
306 "master/doc/"+package+"/rst/")
307 cache = _get_cache(cache)
--> 308 data, from_cache = _get_data(data_base_url, dataname, cache)
309 data = read_csv(data, index_col=0)
310 data = _maybe_reset_index(data)
File /usr/lib/python3/dist-packages/statsmodels/datasets/utils.py:237, in _get_data(base_url, dataname, cache, extension)
235 url = base_url + (dataname + ".%s") % extension
236 try:
--> 237 data, from_cache = _urlopen_cached(url, cache)
238 except HTTPError as err:
239 if "404" in str(err):
File /usr/lib/python3/dist-packages/statsmodels/datasets/utils.py:228, in _urlopen_cached(url, cache)
226 # not using the cache or did not find it in cache
227 if not from_cache:
--> 228 data = urlopen(url, timeout=3).read()
229 if cache is not None: # then put it in the cache
230 _cache_it(data, cache_path)
File /usr/lib/python3.14/urllib/request.py:187, in urlopen(url, data, timeout, context)
185 else:
186 opener = _opener
--> 187 return opener.open(url, data, timeout)
File /usr/lib/python3.14/urllib/request.py:487, in OpenerDirector.open(self, fullurl, data, timeout)
484 req = meth(req)
486 sys.audit('urllib.Request', req.full_url, req.data, req.headers, req.get_method())
--> 487 response = self._open(req, data)
489 # post-process response
490 meth_name = protocol+"_response"
File /usr/lib/python3.14/urllib/request.py:504, in OpenerDirector._open(self, req, data)
501 return result
503 protocol = req.type
--> 504 result = self._call_chain(self.handle_open, protocol, protocol +
505 '_open', req)
506 if result:
507 return result
File /usr/lib/python3.14/urllib/request.py:464, in OpenerDirector._call_chain(self, chain, kind, meth_name, *args)
462 for handler in handlers:
463 func = getattr(handler, meth_name)
--> 464 result = func(*args)
465 if result is not None:
466 return result
File /usr/lib/python3.14/urllib/request.py:1381, in HTTPSHandler.https_open(self, req)
1380 def https_open(self, req):
-> 1381 return self.do_open(http.client.HTTPSConnection, req,
1382 context=self._context)
File /usr/lib/python3.14/urllib/request.py:1336, in AbstractHTTPHandler.do_open(self, http_class, req, **http_conn_args)
1333 h.request(req.get_method(), req.selector, req.data, headers,
1334 encode_chunked=req.has_header('Transfer-encoding'))
1335 except OSError as err: # timeout error
-> 1336 raise URLError(err)
1337 r = h.getresponse()
1338 except:
URLError: <urlopen error [Errno 111] Connection refused>
In [40]: del data["chapter"]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[40], line 1
----> 1 del data["chapter"]
KeyError: 'chapter'
In [41]: data = data.dropna()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[41], line 1
----> 1 data = data.dropna()
AttributeError: 'Dataset' object has no attribute 'dropna'
In [42]: data["lam"] = data["lambda"]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[42], line 1
----> 1 data["lam"] = data["lambda"]
KeyError: 'lambda'
In [43]: data["female"] = (data["sex"] == "F").astype(int)
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[43], line 1
----> 1 data["female"] = (data["sex"] == "F").astype(int)
KeyError: 'sex'
In [44]: data["year"] = data["sample.yr"] - min(data["sample.yr"])
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[44], line 1
----> 1 data["year"] = data["sample.yr"] - min(data["sample.yr"])
KeyError: 'sample.yr'
In [45]: status = data["death"].values
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[45], line 1
----> 1 status = data["death"].values
KeyError: 'death'
In [46]: mod = smf.phreg("futime ~ 0 + age + female + creatinine + np.sqrt(kappa) + np.sqrt(lam) + year + mgus", data, status=status, ties="efron")
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[46], line 1
----> 1 mod = smf.phreg("futime ~ 0 + age + female + creatinine + np.sqrt(kappa) + np.sqrt(lam) + year + mgus", data, status=status, ties="efron")
NameError: name 'status' is not defined
In [47]: rslt = mod.fit()
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[47], line 1
----> 1 rslt = mod.fit()
NameError: name 'mod' is not defined
In [48]: print(rslt.summary())
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[48], line 1
----> 1 print(rslt.summary())
AttributeError: '_Bunch' object has no attribute 'summary'
See Examples for more detailed examples.
There are some notebook examples on the Wiki: Wiki notebooks for PHReg and Survival Analysis
References#
References for Cox proportional hazards regression model:
T Therneau (1996). Extending the Cox model. Technical report.
http://www.mayo.edu/research/documents/biostat-58pdf/DOC-10027288
G Rodriguez (2005). Non-parametric estimation in survival models.
http://data.princeton.edu/pop509/NonParametricSurvival.pdf
B Gillespie (2006). Checking the assumptions in the Cox proportional
hazards model.
http://www.mwsug.org/proceedings/2006/stats/MWSUG-2006-SD08.pdf
Module Reference#
The class for working with survival distributions is:
|
Estimation and inference for a survival function |
|
Estimation and inference for a cumulative incidence function |
survdiff formally compares two or more survival distributions:
|
Test for the equality of two survival distributions |
The proportional hazards regression model class is:
|
Cox Proportional Hazards Regression Model |
The proportional hazards regression result class is:
|
Class to contain results of fitting a Cox proportional hazards survival model |
The primary helper class is:
|
A class representing a collection of discrete distributions |