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dc.contributor.authorPopov, Valentin
dc.contributor.authorEllis-Robinson, Alesha
dc.contributor.authorHumphris, Gerald
dc.identifier.citationPopov , V , Ellis-Robinson , A & Humphris , G 2019 , ' Modelling reassurances of clinicians with Hidden Markov models ' , BMC Medical Research Methodology , vol. 19 , 11 .
dc.identifier.otherPURE: 256683023
dc.identifier.otherPURE UUID: 315aa5be-dd84-41cc-ab1c-0af00ed077dd
dc.identifier.otherScopus: 85059797065
dc.identifier.otherWOS: 000455355600004
dc.identifier.otherORCID: /0000-0002-4601-8834/work/64033907
dc.descriptionGenerous support was received from the charity Breast Cancer Now (grant number: 6873)en
dc.description.abstractBackground: A key element in the interaction between clinicians and patients with cancer is reassurance giving. Learning about the stochastic nature of reassurances as well as making inferential statements about the influence of covariates such as patient response and time spent on previous reassurances are of particular importance. Methods: We fit Hidden Markov Models (HMMs) to reassurance type from multiple time series of clinicians' reassurances, decoded from audio files of review consultations between patients with breast cancer and their therapeutic radiographer. Assuming a latent state process driving the observations process, HMMs naturally accommodate serial dependence in the data. Extensions to the baseline model such as including covariates as well as allowing for fixed effects for the different clinicians are straightforward to implement. Results: We found that clinicians undergo different states, in which they are more or less inclined to provide a particular type of reassurance. The states are very persistent, however switches occasionally occur. The lengthier the previous reassurance, the more likely the clinician is to stay in the current state. Conclusions: HMMs prove to be a valuable tool and provide important insights for practitioners. Trial registration: Trial Registration number: NCT02599506. Prospectively registered on 11th March 2015.
dc.relation.ispartofBMC Medical Research Methodologyen
dc.rightsCopyright © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.en
dc.subjectHidden Markov modelsen
dc.subjectFixed effectsen
dc.subjectQA Mathematicsen
dc.titleModelling reassurances of clinicians with Hidden Markov modelsen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.Centre for Research into Ecological & Environmental Modellingen
dc.contributor.institutionUniversity of St Andrews.Statisticsen
dc.contributor.institutionUniversity of St Andrews.Population and Behavioural Science Divisionen
dc.contributor.institutionUniversity of St Andrews.WHO Collaborating Centre for International Child & Adolescent Health Policyen
dc.contributor.institutionUniversity of St Andrews.Health Psychologyen
dc.contributor.institutionUniversity of St Andrews.St Andrews Sustainability Instituteen
dc.contributor.institutionUniversity of St Andrews.School of Medicineen
dc.contributor.institutionUniversity of St Andrews.Sir James Mackenzie Institute for Early Diagnosisen
dc.description.statusPeer revieweden

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