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Bayesian methods in palliative care research: cancer-induced bone pain
  1. Richard A Parker1,
  2. Tonje A Sande2,
  3. Barry Laird3,
  4. Peter Hoskin4,5,
  5. Marie Fallon3 and
  6. Lesley Colvin6
  1. 1Edinburgh Clinical Trials Unit, Usher Institute, The University of Edinburgh, Edinburgh, UK
  2. 2Usher Institute, The University of Edinburgh, Edinburgh, UK
  3. 3Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
  4. 4Mount Vernon Cancer Centre, Northwood, Middlesex, UK
  5. 5Division of Cancer Sciences, The University of Manchester, Manchester, Manchester, UK
  6. 6Division of Population Health and Genomics, University of Dundee, Dundee, UK
  1. Correspondence to Richard A Parker, Edinburgh Clinical Trials Unit, The University of Edinburgh, Edinburgh EH16 4UX, UK; Richard.parker{at}ed.ac.uk

Abstract

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.

  • methodological research
  • bone
  • cancer
  • pain

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Footnotes

  • RAP and TAS are joint first authors.

  • Contributors RAP wrote the statistical analysis plan, performed the statistical analysis, wrote the first draft of the paper and led the writing of the paper. TAS recruited patients, followed-up patients and performed data management. MF, LC, RAP, BL and PH designed the clinical study. All authors contributed to the writing and/or critical review of the paper.

  • Funding The TiBoP study was funded by Marie Curie [MC grant reference MCCC-RP-15-A19005PI].

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement All data relevant to the methodology study are included in the article and supplementary file. If any further data is required (e.g. relating to the wider TiBoP study) then please send requests to

    ECTUdatashare{at}ed.ac.uk

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