Apr 30, 2003

Exploring heterogeneity in tumour data using Markov chain Monte Carlo

Statistics in Medicine
Mathisca C M de GunstE Georg Luebeck


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.

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  • Citations3
  • References5
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Mentioned in this Paper

Stochastic Processes
Precancerous Conditions
Tumor Lysis Syndrome
Maximum Likelihood Estimates
Monte Carlo Method
Bayesian Prediction
Markov Chains
Posterior Pituitary Disease

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