Mixture Cure Models in Prediction of Time to Default: Comparison with Logit and Cox Models

Wycinka, Ewa; Jurkiewicz, Tomasz

doi:10.1007/978-3-319-54885-2_21

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…Among various modeling approaches for the incidence and the latency, the most common choice is a parametric (logistic) model for the incidence and a semiparametric (Cox or accelerated failure time) model for the latency ( [27,4,15,42,33,39]). The popularity of such a choice is due to the simplicity and ease of interpretation.…”

Section: The Semiparametric Mixture Cure Modelmentioning

confidence: 99%

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A 2-step estimation procedure for semiparametric mixture cure models

Musta¹,

Patilea²,

Keilegom³

2022

Preprint

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Cure models have been developed as an alternative modelling approach to conventional survival analysis in order to account for the presence of cured subjects that will never experience the event of interest. Mixture cure models, which model separately the cure probability and the survival of uncured subjects depending on a set of covariates, are particularly useful for distinguishing curative from life-prolonging effects. In practice, it is common to assume a parametric model for the cure probability and a semiparametric model for the survival of the susceptibles. Because of the latent cure status, maximum likelihood estimation is performed by means of the iterative EM algorithm. Here, we focus on the cure probabilities and propose a two-step procedure to improve upon the performance of the maximum likelihood estimator when the sample size is not large. The new method is based on the idea of presmoothing by first constructing a nonparametric estimator and then projecting it into the desired parametric class. We investigate the theoretical properties of the resulting estimator and show through an extensive simulation study for the logistic-Cox model that it outperforms the existing method. Practical use of the method is illustrated through two melanoma datasets.

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Section: The Semiparametric Mixture Cure Modelmentioning

confidence: 99%

“…These models assume a parametric form of the incidence and a semiparametric form for the latency, with the most common choice being the mixture of the logistic with the Cox proportional hazards model (e.g. [42,33,39]).…”

Section: Introductionmentioning

confidence: 99%

A 2-step estimation procedure for semiparametric mixture cure models

Musta¹,

Patilea²,

Keilegom³

2022

Preprint

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show abstract

“…More recently, nonparametric methods have been developed for both or one of the model components in [2,21,30]. In this wide range of models, probably the most commonly used one in practice is the logistic/Cox mixture cure model [16,24,29].…”

Section: Introductionmentioning

confidence: 99%

A presmoothing approach for estimation in the semiparametric Cox mixture cure model

2022

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A challenge when dealing with survival analysis data is accounting for a cure fraction, meaning that some subjects will never experience the event of interest. Mixture cure models have been frequently used to estimate both the probability of being cured and the time to event for the susceptible subjects, by usually assuming a parametric (logistic) form of the incidence. We propose a new estimation procedure for a parametric cure rate that relies on a preliminary smooth estimator and is independent of the model assumed for the latency. On a second stage one can assume a semiparametric model for the latency and estimate also the survival distribution of the uncured subject. For the particular case of the logistic/Cox model, we investigate the theoretical properties of the estimators and show through simulations that presmoothing leads to more accurate results compared to the maximum likelihood estimator. To illustrate the practical use, we apply the new estimation procedure to two studies of melanoma survival data.

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“…More recently, nonparametric methods have been developed for both or one of the model components in Xu and Peng (2014); Patilea and Van Keilegom (2020); Amico et al (2019). In this wide range of models, probably the most commonly used one in practice is the logistic/Cox mixture cure model (Stringer et al, 2016;Wycinka and Jurkiewicz, 2017;Lee et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

A presmoothing approach for estimation in semiparametric mixture cure models

Musta,

Patilea,

Van Keilegom

2020

Preprint

View full text Add to dashboard Cite

A challenge when dealing with survival analysis data is accounting for a cure fraction, meaning that some subjects will never experience the event of interest. Mixture cure models have been frequently used to estimate both the probability of being cured and the time to event for the susceptible subjects, by usually assuming a parametric (logistic) form of the incidence. We propose a new estimation procedure for a parametric cure rate that relies on a preliminary smooth estimator and is independent of the model assumed for the latency. We investigate the theoretical properties of the estimators and show through simulations that, in the logistic/Cox model, presmoothing leads to more accurate results compared to the maximum likelihood estimator. To illustrate the practical use, we apply the new estimation procedure to two studies of melanoma survival data.

show abstract

Mixture Cure Models in Prediction of Time to Default: Comparison with Logit and Cox Models

Cited by 7 publications

References 15 publications

A 2-step estimation procedure for semiparametric mixture cure models

A 2-step estimation procedure for semiparametric mixture cure models

A presmoothing approach for estimation in the semiparametric Cox mixture cure model

A presmoothing approach for estimation in semiparametric mixture cure models

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