SAS macro PSPMCM: A SAS macro for parametric and semiparametric mixture cure models. Cure models have been developed to analyze failure time data with a cured fraction. 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 individuals, who may experience the event of interest, and non-susceptible individuals that will never experience it. The aim of this paper is to propose a SAS macro to estimate parametric and semiparametric mixture cure models with covariates. The cure fraction can be modelled by various binary regression models. Parametric and semiparametric 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’s proportional hazards mixture cure model. Indications and limitations of the proposed macro are discussed and an example in the field of cancer clinical trials is shown.
Keywords for this software
References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Wycinka, Ewa; Jurkiewicz, Tomasz: Mixture cure models in prediction of time to default: comparison with logit and Cox models (2017)
- Swain, Prafulla Kumar; Grover, Gurprit; Goel, Komal: Mixture and non-mixture cure fraction models based on generalized Gompertz distribution under Bayesian approach (2016)
- Liu, Fan; Hua, Zhongsheng; Lim, Andrew: Identifying future defaulters: a hierarchical Bayesian method (2015)
- Wolter, Marcus; Rösch, Daniel: Cure events in default prediction (2014)
- Tong, Edward N. C.; Mues, Christophe; Thomas, Lyn C.: Mixture cure models in credit scoring: if and when borrowers default (2012)
- Yu, Binbing; Tiwari, Ram C.: A Bayesian approach to mixture cure models with spatial frailties for population-based cancer relative survival data (2012)
- Corbière, Fabien; Joly, Pierre: A SAS macro for parametric and semiparametric mixture cure models. (2007) ioport