Bayesian Survival Analysis

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

doi:10.1007/978-1-4757-3447-8

Cited by 902 publications

(838 citation statements)

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“…A good presentation of Bayesian survival analysis theory and applications can be found in Ibrahim et al [12]. The use of this methodology in conjunction with animal breeding has also gained increased attention lately.…”

Section: Discussionmentioning

confidence: 99%

A genetic and spatial Bayesian analysis of mastitis resistance

Sæbø

Frigessi

2004

Genet. Sel. Evol.

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-A nationwide health card recording system for dairy cattle was introduced in Norway in 1975 (the Norwegian Cattle Health Services). The data base holds information on mastitis occurrences on an individual cow basis. A reduction in mastitis frequency across the population is desired, and for this purpose risk factors are investigated. In this paper a Bayesian proportional hazards model is used for modelling the time to first veterinary treatment of clinical mastitis, including both genetic and environmental covariates. Sire effects were modelled as shared random components, and veterinary district was included as an environmental effect with prior spatial smoothing. A non-informative smoothing prior was assumed for the baseline hazard, and Markov chain Monte Carlo methods (MCMC) were used for inference. We propose a new measure of quality for sires, in terms of their posterior probability of being among the, say 10% best sires. The probability is an easily interpretable measure that can be directly used to rank sires. Estimating these complex probabilities is straightforward in an MCMC setting. The results indicate considerable differences between sires with regards to their daughters disease resistance. A regional effect was also discovered with the lowest risk of disease in the south-eastern parts of Norway. disease resistance / genetic effect / Markov chain Monte Carlo / spatial smoothing / survival analysis

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Section: Discussionmentioning

confidence: 99%

A genetic and spatial Bayesian analysis of mastitis resistance

Sæbø

Frigessi

2004

Genet. Sel. Evol.

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“…The data density is A careful examination of data from earlier studies of survival and cancer [2,15] revealed that the survival distribution could be more accurately represented by a Weibull distribution. The Weibull may be not only the most widely used parametric survival model but with its shape parameter it can be viewed as a generalization of the Exponential [30]. We subsequently denote the survival time as t z where t z has a Weibull distribution with parameters μ z,τ and α.…”

Section: Weibull Transformationmentioning

confidence: 99%

Bayesian Weibull tree models for survival analysis of clinico-genomic data

Clarke

West

2008

Statistical Methodology

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An important goal of research involving gene expression data for outcome prediction is to establish the ability of genomic data to define clinically relevant risk factors. Recent studies have demonstrated that microarray data can successfully cluster patients into low-and high-risk categories. However, the need exists for models which examine how genomic predictors interact with existing clinical factors and provide personalized outcome predictions. We have developed clinico-genomic tree models for survival outcomes which use recursive partitioning to subdivide the current data set into homogeneous subgroups of patients, each with a specific Weibull survival distribution. These trees can provide personalized predictive distributions of the probability of survival for individuals of interest. Our strategy is to fit multiple models; within each model we adopt a prior on the Weibull scale parameter and update this prior via Empirical Bayes whenever the sample is split at a given node. The decision to split is based on a Bayes factor criterion. The resulting trees are weighted according to their relative likelihood values and predictions are made by averaging over models. In a pilot study of survival in advanced stage ovarian cancer we demonstrate that clinical and genomic data are complementary sources of information relevant to survival, and we use the exploratory nature of the trees to identify potential genomic biomarkers worthy of further study.

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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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Bayesian Survival Analysis

Cited by 902 publications

References 0 publications

A genetic and spatial Bayesian analysis of mastitis resistance

A genetic and spatial Bayesian analysis of mastitis resistance

Bayesian Weibull tree models for survival analysis of clinico-genomic data

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

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