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A SAS macro for parametric and semiparametric mixture cure models.

Fabien Corbière1, Pierre Joly

  • 1EMI E0338 Biostatistique, Institut de Santé Publique et Développement, Université Bordeaux 2, 146 rue Léo Saignat, 33076 Bordeaux Cedex, France. fabien.corbiere@isped.u-bordeaux2.fr

Computer Methods and Programs in Biomedicine
|December 13, 2006
PubMed
Summary

This study introduces a SAS macro for analyzing failure time data with a cured fraction using mixture cure models. The macro estimates parametric and semiparametric models, crucial for understanding outcomes in populations with potentially cured individuals.

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Standard survival models are inadequate for failure time data with a cured fraction.
  • Mixture cure models address this by considering susceptible and non-susceptible individuals.
  • Accurate analysis is vital in fields like cancer clinical trials.

Purpose of the Study:

  • To develop and present a SAS macro for estimating mixture cure models.
  • To facilitate the analysis of parametric and semiparametric cure models with covariates.
  • To provide a practical tool for researchers dealing with cured fractions in failure time data.

Main Methods:

  • Development of a SAS macro for mixture cure model estimation.
  • Utilizing SAS PROC NLMIXED for parametric model likelihood maximization.
  • Employing an EM algorithm for the Cox's proportional hazards mixture cure model.
  • Modeling the cure fraction using various binary regression models.

Main Results:

  • The proposed SAS macro enables estimation of both parametric and semiparametric mixture cure models.
  • The macro integrates established statistical procedures (PROC NLMIXED, EM algorithm) for robust analysis.
  • Demonstration of the macro's application using an example from cancer clinical trials.

Conclusions:

  • The SAS macro offers a valuable tool for analyzing failure time data with a cured fraction.
  • It supports flexible modeling of both the cure mechanism and the survival of uncured individuals.
  • The macro enhances the ability to draw accurate conclusions from data where cure is possible.