library.surviv Submodule

Module Summary

Interfaces for the NAG Mark 30.2 surviv Chapter.

surviv - Survival Analysis

This module is concerned with statistical techniques used in the analysis of survival/reliability/failure time data.

Other modules contain functions which are also used to analyse this type of data. Submodule correg contains generalized linear models, submodule univar contains functions to fit distribution models, and submodule nonpar contains rank based methods.

See Also

naginterfaces.library.examples.surviv :

This subpackage contains examples for the surviv module. See also the Examples subsection.

Functionality Index

Cox’s proportional hazard model

create the risk sets: coxmodel_risksets()

parameter estimates and other statistics: coxmodel()

Survival

Rank statistics: logrank()

Survivor function: kaplanmeier()

For full information please refer to the NAG Library document

https://support.nag.com/numeric/nl/nagdoc_30.2/flhtml/g12/g12intro.html

Examples

naginterfaces.library.examples.surviv.coxmodel_ex.main()

Example for naginterfaces.library.surviv.coxmodel().

Fits Cox’s proportional hazard model.

>>> main()
naginterfaces.library.surviv.coxmodel Python Example Results.
Parameter estimates, survival function for a group of leukaemia patients.
Parameter      Estimate       Standard Error
   1           -1.5091            0.4096
Deviance =    1.7276e+02
   Time     Survivor Function
      1        0.9640
      2        0.9264
      3        0.9065
      4        0.8661
      5        0.8235
      6        0.7566
      7        0.7343
      8        0.6506
     10        0.6241
     11        0.5724
     12        0.5135
     13        0.4784
     15        0.4447
     16        0.4078
     17        0.3727
     22        0.2859
     23        0.1908