Bayesian methods in palliative care research: cancer-induced bone pain.

Autor:	Parker RA; Edinburgh Clinical Trials Unit, Usher Institute, The University of Edinburgh, Edinburgh, UK Richard.parker@ed.ac.uk., Sande TA; Usher Institute, The University of Edinburgh, Edinburgh, UK., Laird B; Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK., Hoskin P; Mount Vernon Cancer Centre, Northwood, Middlesex, UK.; Division of Cancer Sciences, The University of Manchester, Manchester, Manchester, UK., Fallon M; Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK., Colvin L; Division of Population Health and Genomics, University of Dundee, Dundee, UK.
Jazyk:	angličtina
Zdroj:	BMJ supportive & palliative care [BMJ Support Palliat Care] 2022 May; Vol. 12 (e1), pp. e5-e9. Date of Electronic Publication: 2020 Mar 05.
DOI:	10.1136/bmjspcare-2019-002160
Abstrakt:	Objective: To show how a simple Bayesian analysis method can be used to improve the evidence base in patient populations where recruitment and retention are challenging. Methods: A Bayesian conjugate analysis method was applied to binary data from the Thermal testing in Bone Pain (TiBoP) study: a prospective diagnostic accuracy/predictive study in patients with cancer-induced bone pain (CIBP). This study aimed to evaluate the clinical utility of a simple bedside tool to identify who was most likely to benefit from palliative radiotherapy (XRT) for CIBP. Results: Recruitment and retention of patients were challenging due to the frail population, with only 27 patients available for the primary analysis. The Bayesian method allowed us to make use of prior work done in this area and combine it with the TiBoP data to maximise the informativeness of the results. Positive and negative predictive values were estimated with greater precision, and interpretation of results was facilitated by use of direct probability statements. In particular, there was only 7% probability that the true positive predictive value was above 80%. Conclusions: Several advantages of using Bayesian analysis are illustrated in this article. The Bayesian method allowed us to gain greater confidence in our interpretation of the results despite the small sample size by allowing us to incorporate data from a previous similar study. We suggest that this method is likely to be useful for the analysis of small diagnostic or predictive studies when prior information is available. Competing Interests: Competing interests: None declared. (© Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ.)
Databáze:	MEDLINE
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