A New Bayesian Model for Survival Data with a Surviving Fraction

Chen, Ming‐Hui; Ibrahim, Joseph G.; Sinha, Debajyoti

doi:10.2307/2670006

Cited by 199 publications

(395 citation statements)

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“…Even though this model is intuitively appealing and widely used, violation of desirable structure of proportional hazards and improper posteriors are major drawbacks of these models (Ibrahim et al, 2001). Chen et al (1999) proposed non-mixture models that approach the problem in a very different way and have a clinical justification. This model, also called parametric cure rate models, assumes a number of metastasiscomponent tumor cells for each cancerous patient.…”

Section: Methodsmentioning

confidence: 99%

Exploring Factors Related to Metastasis Free Survival in Breast Cancer Patients Using Bayesian Cure Models

Jafari‐Koshki

Mansourian²,

Mokarian³

2014

Asian Pacific Journal of Cancer Prevention

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Background: Breast cancer is a fatal disease and the most frequently diagnosed cancer in women with an increasing pattern worldwide. The burden is mostly attributed to metastatic cancers that occur in one-third of patients and the treatments are palliative. It is of great interest to determine factors affecting time from cancer diagnosis to secondary metastasis. Materials and Methods: Cure rate models assume a Poisson distribution for the number of unobservable metastatic-component cells that are completely deleted from the non-metastasis patient body but some may remain and result in metastasis. Time to metastasis is defined as a function of the number of these cells and the time for each cell to develop a detectable sign of metastasis. Covariates are introduced to the model via the rate of metastatic-component cells. We used non-mixture cure rate models with Weibull and log-logistic distributions in a Bayesian setting to assess the relationship between metastasis free survival and covariates. Results: The median of metastasis free survival was 76.9 months. Various models showed that from covariates in the study, lymph node involvement ratio and being progesterone receptor positive were significant, with an adverse and a beneficial effect on metastasis free survival, respectively. The estimated fraction of patients cured from metastasis was almost 48%. The Weibull model had a slightly better performance than log-logistic. Conclusions: Cure rate models are popular in survival studies and outperform other models under certain conditions. We explored the prognostic factors of metastatic breast cancer from a different viewpoint. In this study, metastasis sites were analyzed all together. Conducting similar studies in a larger sample of cancer patients as well as evaluating the prognostic value of covariates in metastasis to each site separately are recommended.

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Section: Methodsmentioning

confidence: 99%

Exploring Factors Related to Metastasis Free Survival in Breast Cancer Patients Using Bayesian Cure Models

Jafari‐Koshki

Mansourian²,

Mokarian³

2014

Asian Pacific Journal of Cancer Prevention

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“…Based on Boag's idea, Berkson and Gage (1952) proposed a mixture model in order to estimate the proportion of cured patients in a population subjected to a treatment of stomach cancer. More complex long-term models, such as Yakovlev and Tsodikov (1996), Chen et al (1999) among others, have emerged in order to better explain the biological effects involved. More recently, proposed a unified theory of long duration, considering different competitive causes.…”

Section: Introductionmentioning

confidence: 99%

Negative Binomial Kumaraswamy-G Cure Rate Regression Model

D’Andrea

Rocha

Tomazella

et al. 2018

JRFM

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Abstract:In survival analysis, the presence of elements not susceptible to the event of interest is very common. These elements lead to what is called a fraction cure, cure rate, or even long-term survivors. In this paper, we propose a unified approach using the negative binomial distribution for modeling cure rates under the Kumaraswamy family of distributions. The estimation is made by maximum likelihood. We checked the maximum likelihood asymptotic properties through some simulation setups. Furthermore, we propose an estimation strategy based on the Negative Binomial Kumaraswamy-G generalized linear model. Finally, we illustrate the distributions proposed using a real data set related to health risk.

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“…The literature on the subject is significant and increasing. Books by Maller and Zhou (1996) and Ibrahim et al (2001), as well as the review paper by Chen et al (1999), Tsodikov et al (2003), and the article by Cooner et al (2007) represent key references. Alternatively, other works dealt with cure rate models.…”

Section: Introductionmentioning

confidence: 99%

Regression models generated by gamma random variables with long-term survivors

Ortega

Cordeiro

Hashimoto

et al. 2017

CSAM

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We propose a flexible cure rate survival model by assuming that the number of competing causes of the event of interest has the Poisson distribution and the time for the event follows the gamma-G family of distributions. The extended family of gamma-G failure-time models with long-term survivors is flexible enough to include many commonly used failure-time distributions as special cases. We consider a frequentist analysis for parameter estimation and derive appropriate matrices to assess local influence on the parameters. Further, various simulations are performed for different parameter settings, sample sizes and censoring percentages. We illustrate the performance of the proposed regression model by means of a data set from the medical area (gastric cancer).

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A New Bayesian Model for Survival Data with a Surviving Fraction

Cited by 199 publications

References 0 publications

Exploring Factors Related to Metastasis Free Survival in Breast Cancer Patients Using Bayesian Cure Models

Exploring Factors Related to Metastasis Free Survival in Breast Cancer Patients Using Bayesian Cure Models

Negative Binomial Kumaraswamy-G Cure Rate Regression Model

Regression models generated by gamma random variables with long-term survivors

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