Symptom clusters in advanced cancer patients

An empirical comparison of statistical methods and the impact on quality of life

Skye T. Dong*, Daniel S.J. Costa, Phyllis N. Butow, Melanie R. Lovell, Meera Agar, Galina Velikova, Paulos Teckle, Allison Tong, Niall C. Tebbutt, Stephen J. Clarke, Kim Van Der Hoek, Madeleine T. King, Peter M. Fayers

*Corresponding author for this work

Research output: Contribution to journalArticle

37 Citations (Scopus)

Abstract

Context Symptom clusters in advanced cancer can influence patient outcomes. There is large heterogeneity in the methods used to identify symptom clusters. 

Objectives To investigate the consistency of symptom cluster composition in advanced cancer patients using different statistical methodologies for all patients across five primary cancer sites, and to examine which clusters predict functional status, a global assessment of health and global quality of life. 

Methods Principal component analysis and exploratory factor analysis (with different rotation and factor selection methods) and hierarchical cluster analysis (with different linkage and similarity measures) were used on a data set of 1562 advanced cancer patients who completed the European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire-Core 30. 

Results Four clusters consistently formed for many of the methods and cancer sites: tense-worry-irritable-depressed (emotional cluster), fatigue-pain, nausea-vomiting, and concentration-memory (cognitive cluster). The emotional cluster was a stronger predictor of overall quality of life than the other clusters. Fatigue-pain was a stronger predictor of overall health than the other clusters. The cognitive cluster and fatigue-pain predicted physical functioning, role functioning, and social functioning.

 Conclusions The four identified symptom clusters were consistent across statistical methods and cancer types, although there were some noteworthy differences. Statistical derivation of symptom clusters is in need of greater methodological guidance. A psychosocial pathway in the management of symptom clusters may improve quality of life. Biological mechanisms underpinning symptom clusters need to be delineated by future research. A framework for evidence-based screening, assessment, treatment, and follow-up of symptom clusters in advanced cancer is essential.

Original languageEnglish
Pages (from-to)88-98
Number of pages11
JournalJournal of Pain and Symptom Management
Volume51
Issue number1
Early online date21 Aug 2015
DOIs
Publication statusPublished - Jan 2016

Fingerprint

Quality of Life
Neoplasms
Fatigue
Pain
Principal Component Analysis
Nausea
Statistical Factor Analysis
Vomiting
Cluster Analysis
Organizations
Health
Therapeutics
Research

Keywords

  • advanced cancer
  • EORTC QLQ-C30
  • quality of life
  • statistical methods
  • Symptom clusters

ASJC Scopus subject areas

  • Nursing(all)
  • Clinical Neurology
  • Anesthesiology and Pain Medicine

Cite this

Symptom clusters in advanced cancer patients : An empirical comparison of statistical methods and the impact on quality of life. / Dong, Skye T.; Costa, Daniel S.J.; Butow, Phyllis N.; Lovell, Melanie R.; Agar, Meera; Velikova, Galina; Teckle, Paulos; Tong, Allison; Tebbutt, Niall C.; Clarke, Stephen J.; Van Der Hoek, Kim; King, Madeleine T.; Fayers, Peter M.

In: Journal of Pain and Symptom Management, Vol. 51, No. 1, 01.2016, p. 88-98.

Research output: Contribution to journalArticle

Dong, ST, Costa, DSJ, Butow, PN, Lovell, MR, Agar, M, Velikova, G, Teckle, P, Tong, A, Tebbutt, NC, Clarke, SJ, Van Der Hoek, K, King, MT & Fayers, PM 2016, 'Symptom clusters in advanced cancer patients: An empirical comparison of statistical methods and the impact on quality of life', Journal of Pain and Symptom Management, vol. 51, no. 1, pp. 88-98. https://doi.org/10.1016/j.jpainsymman.2015.07.013
Dong, Skye T. ; Costa, Daniel S.J. ; Butow, Phyllis N. ; Lovell, Melanie R. ; Agar, Meera ; Velikova, Galina ; Teckle, Paulos ; Tong, Allison ; Tebbutt, Niall C. ; Clarke, Stephen J. ; Van Der Hoek, Kim ; King, Madeleine T. ; Fayers, Peter M. / Symptom clusters in advanced cancer patients : An empirical comparison of statistical methods and the impact on quality of life. In: Journal of Pain and Symptom Management. 2016 ; Vol. 51, No. 1. pp. 88-98.
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abstract = "Context Symptom clusters in advanced cancer can influence patient outcomes. There is large heterogeneity in the methods used to identify symptom clusters. Objectives To investigate the consistency of symptom cluster composition in advanced cancer patients using different statistical methodologies for all patients across five primary cancer sites, and to examine which clusters predict functional status, a global assessment of health and global quality of life. Methods Principal component analysis and exploratory factor analysis (with different rotation and factor selection methods) and hierarchical cluster analysis (with different linkage and similarity measures) were used on a data set of 1562 advanced cancer patients who completed the European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire-Core 30. Results Four clusters consistently formed for many of the methods and cancer sites: tense-worry-irritable-depressed (emotional cluster), fatigue-pain, nausea-vomiting, and concentration-memory (cognitive cluster). The emotional cluster was a stronger predictor of overall quality of life than the other clusters. Fatigue-pain was a stronger predictor of overall health than the other clusters. The cognitive cluster and fatigue-pain predicted physical functioning, role functioning, and social functioning. Conclusions The four identified symptom clusters were consistent across statistical methods and cancer types, although there were some noteworthy differences. Statistical derivation of symptom clusters is in need of greater methodological guidance. A psychosocial pathway in the management of symptom clusters may improve quality of life. Biological mechanisms underpinning symptom clusters need to be delineated by future research. A framework for evidence-based screening, assessment, treatment, and follow-up of symptom clusters in advanced cancer is essential.",
keywords = "advanced cancer, EORTC QLQ-C30, quality of life, statistical methods, Symptom clusters",
author = "Dong, {Skye T.} and Costa, {Daniel S.J.} and Butow, {Phyllis N.} and Lovell, {Melanie R.} and Meera Agar and Galina Velikova and Paulos Teckle and Allison Tong and Tebbutt, {Niall C.} and Clarke, {Stephen J.} and {Van Der Hoek}, Kim and King, {Madeleine T.} and Fayers, {Peter M.}",
note = "Disclosures and Acknowledgments No funding was received for this study. Professor Butow was supported by an NHMRC fellowship. Professor King was supported by the Australian Government through Cancer Australia. The other authors declare no conflicts of interest. The authors thank the following individuals and organizations for their generosity in contributing data for secondary analysis: U. Abacioglu, J. Arraras, J. Blazeby, Canadian Centre for Applied Research in Cancer Control/BC Cancer Agency, S. Clarke, G. de Castro, S. Kaasa, P. Klepstad, Millennium Pharmaceuticals, K. Mystakidou, S. Peacock, N. Scott, N. Tebbutt, G. Velikova, and the Australasian GastroIntestinal Trials Group.",
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T1 - Symptom clusters in advanced cancer patients

T2 - An empirical comparison of statistical methods and the impact on quality of life

AU - Dong, Skye T.

AU - Costa, Daniel S.J.

AU - Butow, Phyllis N.

AU - Lovell, Melanie R.

AU - Agar, Meera

AU - Velikova, Galina

AU - Teckle, Paulos

AU - Tong, Allison

AU - Tebbutt, Niall C.

AU - Clarke, Stephen J.

AU - Van Der Hoek, Kim

AU - King, Madeleine T.

AU - Fayers, Peter M.

N1 - Disclosures and Acknowledgments No funding was received for this study. Professor Butow was supported by an NHMRC fellowship. Professor King was supported by the Australian Government through Cancer Australia. The other authors declare no conflicts of interest. The authors thank the following individuals and organizations for their generosity in contributing data for secondary analysis: U. Abacioglu, J. Arraras, J. Blazeby, Canadian Centre for Applied Research in Cancer Control/BC Cancer Agency, S. Clarke, G. de Castro, S. Kaasa, P. Klepstad, Millennium Pharmaceuticals, K. Mystakidou, S. Peacock, N. Scott, N. Tebbutt, G. Velikova, and the Australasian GastroIntestinal Trials Group.

PY - 2016/1

Y1 - 2016/1

N2 - Context Symptom clusters in advanced cancer can influence patient outcomes. There is large heterogeneity in the methods used to identify symptom clusters. Objectives To investigate the consistency of symptom cluster composition in advanced cancer patients using different statistical methodologies for all patients across five primary cancer sites, and to examine which clusters predict functional status, a global assessment of health and global quality of life. Methods Principal component analysis and exploratory factor analysis (with different rotation and factor selection methods) and hierarchical cluster analysis (with different linkage and similarity measures) were used on a data set of 1562 advanced cancer patients who completed the European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire-Core 30. Results Four clusters consistently formed for many of the methods and cancer sites: tense-worry-irritable-depressed (emotional cluster), fatigue-pain, nausea-vomiting, and concentration-memory (cognitive cluster). The emotional cluster was a stronger predictor of overall quality of life than the other clusters. Fatigue-pain was a stronger predictor of overall health than the other clusters. The cognitive cluster and fatigue-pain predicted physical functioning, role functioning, and social functioning. Conclusions The four identified symptom clusters were consistent across statistical methods and cancer types, although there were some noteworthy differences. Statistical derivation of symptom clusters is in need of greater methodological guidance. A psychosocial pathway in the management of symptom clusters may improve quality of life. Biological mechanisms underpinning symptom clusters need to be delineated by future research. A framework for evidence-based screening, assessment, treatment, and follow-up of symptom clusters in advanced cancer is essential.

AB - Context Symptom clusters in advanced cancer can influence patient outcomes. There is large heterogeneity in the methods used to identify symptom clusters. Objectives To investigate the consistency of symptom cluster composition in advanced cancer patients using different statistical methodologies for all patients across five primary cancer sites, and to examine which clusters predict functional status, a global assessment of health and global quality of life. Methods Principal component analysis and exploratory factor analysis (with different rotation and factor selection methods) and hierarchical cluster analysis (with different linkage and similarity measures) were used on a data set of 1562 advanced cancer patients who completed the European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire-Core 30. Results Four clusters consistently formed for many of the methods and cancer sites: tense-worry-irritable-depressed (emotional cluster), fatigue-pain, nausea-vomiting, and concentration-memory (cognitive cluster). The emotional cluster was a stronger predictor of overall quality of life than the other clusters. Fatigue-pain was a stronger predictor of overall health than the other clusters. The cognitive cluster and fatigue-pain predicted physical functioning, role functioning, and social functioning. Conclusions The four identified symptom clusters were consistent across statistical methods and cancer types, although there were some noteworthy differences. Statistical derivation of symptom clusters is in need of greater methodological guidance. A psychosocial pathway in the management of symptom clusters may improve quality of life. Biological mechanisms underpinning symptom clusters need to be delineated by future research. A framework for evidence-based screening, assessment, treatment, and follow-up of symptom clusters in advanced cancer is essential.

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