PiecewiseExponentialFitter

class lifelines.fitters.piecewise_exponential_fitter.PiecewiseExponentialFitter(breakpoints, *args, **kwargs)

This class implements an Piecewise Exponential model for univariate data. The model has parameterized hazard rate:

\[\begin{split}h(t) = \begin{cases} 1/\lambda_0 & \text{if $t \le \tau_0$} \\ 1/\lambda_1 & \text{if $\tau_0 < t \le \tau_1$} \\ 1/\lambda_2 & \text{if $\tau_1 < t \le \tau_2$} \\ ... \end{cases}\end{split}\]

You specify the breakpoints, \(\tau_i\), and lifelines will find the optional values for the parameters.

After calling the .fit method, you have access to properties like: survival_function_, plot, cumulative_hazard_ A summary of the fit is available with the method print_summary()

Parameters:

• breakpoints (list) – a list of times when a new exponential model is constructed.

• alpha (float, optional (default=0.05)) – the level in the confidence intervals.

cumulative_hazard_

The estimated cumulative hazard (with custom timeline if provided)

Type:

DataFrame

hazard_

The estimated hazard (with custom timeline if provided)

Type:

DataFrame

survival_function_

The estimated survival function (with custom timeline if provided)

Type:

DataFrame

cumulative_density_

The estimated cumulative density function (with custom timeline if provided)

Type:

DataFrame

density_

The estimated density function (PDF) (with custom timeline if provided)

Type:

DataFrame

variance_matrix_

The variance matrix of the coefficients

Type:

DataFrame

median_survival_time_

The median time to event

Type:

float

lambda_i_

The fitted parameter in the model, for i = 0, 1 … n-1 breakpoints

Type:

float

durations

The durations provided

Type:

array

event_observed

The event_observed variable provided

Type:

array

timeline

The time line to use for plotting and indexing

Type:

array

entry

The entry array provided, or None

Type:

array or None

breakpoints

The provided breakpoints

Type:

array