Bayesian accelerated failure time analysis with application to veterinary epidemiology

Bedrick, Edward J.; Christensen, Ronald; Johnson, Wesley O.

doi:10.1002/(sici)1097-0258(20000130)19:2<221::aid-sim328>3.0.co;2-c

Cited by 26 publications

(10 citation statements)

References 37 publications

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“…Moreover, if they also had scientific information about the log normal family parameters, they could construct an informative prior for those parameters. See Bedrick et al (1996) and Bedrick et al (2000) for illustrations of informative prior specification for generalized linear models and for survival models. Thus far, we are not aware of any such nice properties for specifying prior distributions on the parameters of the base distribution in the DPM.…”

Section: Comments On the Dpm And Mptmentioning

confidence: 99%

Bayesian Nonparametric Biostatistics

Johnson

Carvalho

2015

Nonparametric Bayesian Inference in Biostatistics

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We discuss some typical applications of Bayesian nonparametrics in biostatistics. The chosen applications highlight how Bayesian nonparametrics can contribute to addressing some fundamental questions that arise in biomedical research. In particular, we review some modern Bayesian semi-and nonparametric approaches for modeling longitudinal, survival, and medical diagnostic outcome data. Our discussion includes methods for longitudinal data analysis, non-proportional hazards survival analysis, joint modeling of longitudinal and survival data, longitudinal diagnostic test outcome data, and receiver operating characteristic curves. Throughout, we make comparisons among competing BNP models for the various data types considered.

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Section: Comments On the Dpm And Mptmentioning

confidence: 99%

Bayesian Nonparametric Biostatistics

Johnson

Carvalho

2015

Nonparametric Bayesian Inference in Biostatistics

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“…When

{boldw}_{J} = {bold 1}_{J} / J

, that is,

{boldw}_{J}

is fixed at its prior mean, the Weibull regression model is obtained. Thus any prior specification approach for Weibull regression would reasonably work, for example the conditional median approach of Bedrick, Christensen, and Johnson ().…”

Section: Transformed Bernstein Polynomial Priorsmentioning

confidence: 99%

Accelerated hazards model based on parametric families generalized with Bernstein polynomials

2013

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Summary A transformed Bernstein polynomial that is centered at standard parametric families, such as Weibull or log-logistic, is proposed for use in the accelerated hazards model. This class provides a convenient way towards creating a Bayesian non-parametric prior for smooth densities, blending the merits of parametric and non-parametric methods, that is amenable to standard estimation approaches. For example optimization methods in SAS or R can yield the posterior mode and asymptotic covariance matrix. This novel nonparametric prior is employed in the accelerated hazards model, which is further generalized to time-dependent covariates. The proposed approach fares considerably better than previous approaches in simulations; data on the effectiveness of biodegradable carmustine polymers on recurrent brain malignant gliomas is investigated.

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“…Here m = 0 and S = 1000 I p +2 . Prior information could be elicited and implemented based on parametric model (Bedrick et al, 2000). …”

Section: Statistical Modelsmentioning

confidence: 99%

Mixtures of Polya trees for flexible spatial frailty survival modelling

2009

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Abstract Mixtures of Polya trees offer a very flexible nonparametric approach for modelling time-to-event data. Many such settings also feature spatial association that requires further sophistication, either at the point level or at the lattice level. In this paper, we combine these two aspects within three competing survival models, obtaining a data analytic approach that remains computationally feasible in a fully hierarchical Bayesian framework using Markov chain Monte Carlo methods. We illustrate our proposed methods with an analysis of spatially oriented breast cancer survival data from the Surveillance, Epidemiology and End Results program of the National Cancer Institute. Our results indicate appreciable advantages for our approach over competing methods that impose unrealistic parametric assumptions, ignore spatial association or both.

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Bayesian accelerated failure time analysis with application to veterinary epidemiology

Cited by 26 publications

References 37 publications

Bayesian Nonparametric Biostatistics

Bayesian Nonparametric Biostatistics

Accelerated hazards model based on parametric families generalized with Bernstein polynomials

Mixtures of Polya trees for flexible spatial frailty survival modelling

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