<scp>B</scp>ayesian Survival Analysis

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

doi:10.1002/9781118445112.stat06003

Cited by 166 publications

(74 citation statements)

References 85 publications

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“…After initial treatments, these cells are completely deleted in cured patients. However, some of these cells are left in the body of the other patients and by passing the time cause the metastasis in these patients of uncured group (Ibrahim et al, 2001). It is obvious that metastasis free survival is affected by both the number of left over cells and their activity.…”

Section: Methodsmentioning

confidence: 99%

“…In this framework, there is an opportunity to assess two different types of covariate impact on survival and being cured (Ibrahim et al, 2001). In this format, a covariate can be assessed in terms of being effective or not both in survival and being cured.…”

Section: Methodsmentioning

confidence: 99%

“…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.…”

Section: Methodsmentioning

confidence: 99%

“…There is a major difference between these approaches, however. When covariates c are introduced to models via q, proportionality holds for non-mixture model of S (2) pop only (Ibrahim et al, 2001). Unlike non-mixture models, odds ratio (OR), rather than hazard ratio (HR), is used in mixture models to assess the impact of a covariate on cure fractions.…”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

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%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

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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“…The most common approach is to suppose that a random effect underlines both longitudinal and survival outcomes as shared parameter models (Henderson et al [5]; Hashemi et al [5]). Also some other researches are done on Bayesian method (Ibrahim et al [7]; Chi and Ibrahim [8]), nonparametric random effect distributions (Wang and Taylor [9]; Brown and Ibrahim [3]), cure fractions (Yu et al [10]; Chi and Ibrahim [8]), multiple longitudinal variables (Lin et al [11]), count data (Dunson and Herring [12]), zeroinflated outcomes (Rizopoulos et al [13]), competing risks (Elashoff et al [14]), accelerated failure time (Tseng et al [15]), parametric assumptions by allowing flexible longitudinal trends (Brown et al [16]). In joint modeling of longitudinal and survival data a mixed effects model is often used for analyzing longitudinal response of joint modeling.…”

mentioning

confidence: 99%

Bayesian Analysis of Joint Modeling of Longitudinal and Time to Event Data Using Some Skew-Elliptical Distributions

Khoundabi¹,

Kazemnejad²,

Mansourian³

et al. 2017

BBIJ

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Joint modeling of longitudinal and time to event data have been widely used for analyzing medical data, where longitudinal measurements is gathered with a time to event or survival data. In most of these studies, distributional assumption for modeling longitudinal response is normal, which leads to vulnerable inference in the presence of outliers in longitudinal measurements and violation of this assumption. Violation of the normality assumption can also make the statistical inference vague. Powerful distributions for robust analyzing and relaxing normality assumption, are skew-elliptical distributions, which include univariate and multivariate versions of the student's t, the Laplace and normal distributions. In this paper, a linear mixed effects model with skew-elliptical distribution for both random effects and residuals and a Cox's model for time to event data are used for joint modeling. This strategy allows for the skewness and the heavy tails of random effect distributions and thus makes inferences robust to the violation. For estimation, a Bayesian parametric approach using Markov chain Monte Carlo is adopted. The method is illustrated in a real Intensive Care Unit (ICU) data set and the best model is selected using some Bayesian criteria for model selection.

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Using EM and Data Augmentation for the Competing Risks Model

Craiu

Duchesne

2004

Wiley Series in Probability and Statistics

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We consider a survival analysis problem in which items are subject to failure from competing risks. For some of the items, the failure cause is known only to belong to a subset of the set of all possible causes, while for the remaining items the cause of death is known precisely. In this chapter we investigate two complementary analyses based on models in which the hazard rates are assumed piecewise constant. The approaches proposed rely on the EM algorithm and its Bayesian counterpart, the data augmentation (DA) algorithm. An example is used to illustrate the advantages of each analysis.

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Bayesian Survival Analysis

Cited by 166 publications

References 85 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

Bayesian Analysis of Joint Modeling of Longitudinal and Time to Event Data Using Some Skew-Elliptical Distributions

Using EM and Data Augmentation for the Competing Risks Model

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