Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/22881
Title: Using cluster analysis of anxiety-depression to identify subgroups of prostate cancer patients for targeted treatment planning
Contributor(s): Sharpley, Christopher  (author)orcid ; Bitsika, Vicki  (author)orcid ; Warren, Amelia K (author); Christie, David R H  (author)
Publication Date: 2017
DOI: 10.1002/pon.4391
Handle Link: https://hdl.handle.net/1959.11/22881
Abstract: Background: To explore any possible subgroupings of prostate cancer (PCa) patients based upon their combined anxiety‐depression symptoms for the purposes of informing targeted treatments. Methods: A sample of 119 PCa patients completed the GAD7 (anxiety) and PHQ9 (depression), plus a background questionnaire, by mail survey. Data on the GAD7 and PHQ9 were used in a cluster analysis procedure to identify and define any cohesive subgroupings of patients within the sample. Results: Three distinct clusters of patients were identified and were found to be significantly different in the severity of their GAD7 and PHQ9 responses, and also by the profile of symptoms that they exhibited. Conclusions: The presence of these 3 clusters of PCa patients indicates that there is a need to extend assessment of anxiety and depression in these men beyond simple total score results. By applying the clustering profiles to samples of PCa patients, more focussed treatment might be provided to them, hopefully improving outcome efficacy.
Publication Type: Journal Article
Source of Publication: Psycho-Oncology, 26(11), p. 1846-1851
Publisher: John Wiley & Sons Ltd
Place of Publication: United Kingdom
ISSN: 1099-1611
1057-9249
Fields of Research (FoR) 2008: 110903 Central Nervous System
Fields of Research (FoR) 2020: 320903 Central nervous system
Socio-Economic Objective (SEO) 2008: 920111 Nervous System and Disorders
Socio-Economic Objective (SEO) 2020: 200409 Mental health
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

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