Exploring heterogeneity in tumour data using Markov chain Monte Carlo

M.C.M. de Gunst, A. Dewanji, E.G. Luebeck

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

We describe a Bayesian approach to incorporate between-individual heterogeneity associated with parameters of complicated biological models. We emphasize the use of the Markov chain Monte Carlo (MCMC) method in this context and demonstrate the implementation and use of MCMC by analysis of simulated overdispersed Poisson counts and by analysis of an experimental data set on preneoplastic liver lesions (their number and sizes) in the presence of heterogeneity. These examples show that MCMC-based estimates, derived from the posterior distribution with uniform priors, may agree well with maximum likelihood estimates (if available). However, with heterogeneous parameters, maximum likelihood estimates can be difficult to obtain, involving many integrations. In this case, the MCMC method offers substantial computational advantages. Copyright © 2003 John Wiley & Sons, Ltd.
Original languageEnglish
Pages (from-to)1691-1707
JournalStatistics in Medicine
Volume22
Issue number10
DOIs
Publication statusPublished - 2003

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Markov Chains
Markov Chain Monte Carlo Methods
Markov Chain Monte Carlo
Maximum Likelihood Estimate
Tumor
Likelihood Functions
Monte Carlo Method
Biological Models
Posterior distribution
Bayesian Approach
Liver
Neoplasms
Siméon Denis Poisson
Count
Experimental Data
Bayes Theorem
Estimate
Demonstrate
Context

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de Gunst, M.C.M. ; Dewanji, A. ; Luebeck, E.G. / Exploring heterogeneity in tumour data using Markov chain Monte Carlo. In: Statistics in Medicine. 2003 ; Vol. 22, No. 10. pp. 1691-1707.
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Exploring heterogeneity in tumour data using Markov chain Monte Carlo. / de Gunst, M.C.M.; Dewanji, A.; Luebeck, E.G.

In: Statistics in Medicine, Vol. 22, No. 10, 2003, p. 1691-1707.

Research output: Contribution to JournalArticleAcademicpeer-review

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