Abstract
Background: 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: ClinicalTrials.gov: NCT02599506. Prospectively registered on 11th March 2015.
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: ClinicalTrials.gov: NCT02599506. Prospectively registered on 11th March 2015.
| Original language | English |
|---|---|
| Article number | 11 |
| Number of pages | 10 |
| Journal | BMC Medical Research Methodology |
| Volume | 19 |
| DOIs | |
| Publication status | Published - 9 Jan 2019 |
Keywords
- Reassurance
- Hidden Markov models
- Fixed effects
Access to Document
- Popov_2018_BMCMRM_Reassurances_CC
Copyright © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), 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 (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
