We have over 12,000 students, from over 100 countries, within one of the safest campuses in the UK


93% of Lancaster students go into work or further study within six months of graduating

Home > Research > Publications & Outputs > Model diagnostics for multi-state models.
View graph of relations

« Back

Model diagnostics for multi-state models.

Research output: Contribution to journalJournal article


Journal publication date08/2009
JournalStatistical Methods in Medical Research
Original languageEnglish


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.