Input/Output

Compute the log-rank P-value and survival curves based on kaplan meier.

param time_event:

Numpy array with survival-time in years/months/days (not a datetime!)

type time_event:

Float.

param censoring:

numpy array with censoring (1=event, 0=ongoing). At the time you want to make inferences about durations, it is possible, likely true, that not all the death events have occured yet. In case of patients, you would like to put 1=death as an event.

type censoring:

array-like

param labx:

Class labels. Each class label is seperately plotted.

type labx:

array-like of type string or int

param verbose:

Verbosity messages.

type verbose:

int, (default: 3)

returns:

• logrank_P (float) : P-value

• logrank_Z (float) : Z-score

• logrank (float) : fitted logrank_test model

• labx (list) : Class labels

• uilabx (list) : Unique Class labels

• time_event (float) : Time to event

• censoring (bool) : Censored or not

rtype:

dict()

Examples

>>> # Import library
>>> import kaplanmeier as km
>>>
>>> # Example data
>>> df = km.example_data()
>>>
>>> # Fit
>>> results = km.fit(df['time'], df['Died'], df['group'])
>>>
>>> # Plot
>>> km.plot(results)
>>>
>>> km.plot(results, cmap='Set1', cii_lines=True, cii_alpha=0.05)
>>> km.plot(results, cmap=[(1, 0, 0),(0, 0, 1)])
>>> km.plot(results, cmap='Set1', methodtype='custom')
>>>
>>> results['logrank_P']
>>> results['logrank_Z']

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