Presentation

The SAS macro PSPMCM consists in estimating models for failure time data with a cured fraction. Such data may arise from clinical trials, in which, even after an extended follow-up, no further events of interest are observed. For such data there is also a time window within which almost all the events occur. Typically the Kaplan-Meier estimate of the survival function for time to event levels off to nonzero proportions with a heavy censoring at the tail after a sufficient monitoring length. For such data, standard survival models are usually not appropriate because they do not account for the possibility of cure. Mixture cure models assume that the studied population is a mixture of susceptible (uncured) individuals, that may undergo the event of interest, and non-susceptible (cured) individuals, that will never undergo it. This approach allows estimating simultaneously whether the event of interest will occur, which is called incidence, and when it will occur, given that it can occur, which is called latency. Depending on the parametric and semiparametric specification of the survival function for uncured individuals, various mixture cure models have been proposed.

The PSPMCM SAS macro can estimate parametric and semiparametric mixture cure models with covariates. The cure fraction can be modelled by various binary regression models. Parametric and semiparametric Cox proportional hazard models can be used to model the survival of uncured individuals. The maximization of the likelihood function is performed using SAS PROC NLMIXED for parametric models and through an EM algorithm for the Cox PH mixture cure model. The variances of parameters estimates are computed using the inverse of the Hessian matrix or approximated by bootstrap methods.

The compacted file PSPMCM.zip is downloadable and contains the following files:

• PSPMCM.sas: SAS code for the macro
• PSPMCM.pdf: short presentation using a simulated dataset
• SIMULTEX.sas7bdat: a simulated data set in SAS dataset format
• SIMULTEX.txt: the same dataset in text format.

References

Corbière F, Joly P.
A SAS macro for parametric and semiparametric mixture cure models. Comput Methods Programs Biomed 2007;85(2):173-80

Author

Fabien CORBIERE
Pierre JOLY
Inserm U897
146 rue Léo Saignat
33076 Bordeaux Cedex
France

Contact

E-mail: Pierre.Joly@isped.u-bordeaux2.fr.
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Licence

This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.