Journal ArticleDOI
Bayesian incidence analysis of animal tumorigenicity data
David B. Dunson,Gregg E. Dinse +1 more
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This paper proposes a Bayesian method for analysing data from animal carcinogenicity experiments and accommodates occult tumours and censored onset times without restricting tumour lethality, relying on cause‐of‐death data, or requiring interim sacrifices.Abstract:
Statistical inference about tumorigenesis should focus on the tumour incidence rate. Unfortunately, in most animal carcinogenicity experiments, tumours are not observable in live animals and censoring of the tumour onset times is informative. In this paper, we propose a Bayesian method for analysing data from such studies. Our approach focuses on the incidence of tumours and accommodates occult tumours and censored onset times without restricting tumour lethality, relying on cause-of-death data, or requiring interim sacrifices. We represent the underlying state of nature by a multistate stochastic process and assume general probit models for the time-specific transition rates. These models allow the incorporation of covariates, historical control data and subjective prior information. The inherent flexibility of this approach facilitates the interpretation of results, particularly when the sample size is small or the data are sparse. We use a Gibbs sampler to estimate the relevant posterior distributions. The methods proposed are applied to data from a US National Toxicology Program carcinogenicity study.read more
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Best practices for use of historical control data of proliferative rodent lesions.
Charlotte M. Keenan,Susan A. Elmore,Sabine Francke-Carroll,Ramon K. Kemp,Roy L. Kerlin,Shyamal D. Peddada,John M. Pletcher,Matthias Rinke,Stephen P. Schmidt,Ian Taylor,Douglas C. Wolf +10 more
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Incorporating Historical Control Data When Comparing Tumor Incidence Rates
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References
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Journal ArticleDOI
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Stuart Geman,Donald Geman +1 more
TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
Journal ArticleDOI
Random-effects models for longitudinal data
Nan M. Laird,James H. Ware +1 more
TL;DR: In this article, a unified approach to fitting two-stage random-effects models, based on a combination of empirical Bayes and maximum likelihood estimation of model parameters and using the EM algorithm, is discussed.
Book
Pathologic basis of disease
TL;DR: The objective is to establish an experimental procedure and show direct AFM progression from EMT to EMT using a simple, straightforward, and reproducible procedure.
Journal ArticleDOI
Markov Chains for Exploring Posterior Distributions
TL;DR: Several Markov chain methods are available for sampling from a posterior distribution as discussed by the authors, including Gibbs sampler and Metropolis algorithm, and several strategies for constructing hybrid algorithms, which can be used to guide the construction of more efficient algorithms.
Journal ArticleDOI
Bayesian analysis of binary and polychotomous response data
Jim Albert,Siddhartha Chib +1 more
TL;DR: In this paper, exact Bayesian methods for modeling categorical response data are developed using the idea of data augmentation, which can be summarized as follows: the probit regression model for binary outcomes is seen to have an underlying normal regression structure on latent continuous data, and values of the latent data can be simulated from suitable truncated normal distributions.
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