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Model diagnostics for multi-state models

¹ Department of Mathematics and Statistics, Lancaster University, UK
² MRC Biostatistics Unit, Cambridge, UK

^* To whom correspondence should be addressed. E-mail: a.titman{at}lancaster.ac.uk.

	Abstract

Multi-state models are a popular method of describing medical processes that can be represented as discrete states or stages. They have particular use when the data are panel-observed, meaning they consist of discrete snapshots of disease status at irregular time points which may be unique to each patient. However, due to the difficulty of inference in more complicated cases, strong assumptions such as the Markov property, patient homogeneity and time homogeneity are applied. It is important that the validity of these assumptions is tested. A review of methods for diagnosing model fit for panel-observed continuous-time Markov and misclassification-type hidden Markov models is given, with illustrative application to a dataset on cardiac allograft vasculopathy progression in post-heart transplant patients.

First published on August 4, 2009
Statistical Methods in Medical Research 2009, doi:10.1177/0962280209105541


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