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

doi:10.1016/j.jpainsymman.2015.07.013

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 journal › Article › peer-review

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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.

Original language	English
Pages (from-to)	88-98
Number of pages	11
Journal	Journal of Pain and Symptom Management
Volume	51
Issue number	1
Early online date	21 Aug 2015
DOIs	https://doi.org/10.1016/j.jpainsymman.2015.07.013
Publication status	Published - Jan 2016

Bibliographical 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.

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Dong, S. T., Costa, D. S. J., Butow, P. N., Lovell, M. R., Agar, M., Velikova, G., Teckle, P., Tong, A., Tebbutt, N. C., Clarke, S. J., Van Der Hoek, K., King, M. T., & Fayers, P. M. (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, 51(1), 88-98. https://doi.org/10.1016/j.jpainsymman.2015.07.013

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

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title = "Symptom clusters in advanced cancer patients: An empirical comparison of statistical methods and the impact on quality of life",

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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language = "English",

volume = "51",

pages = "88--98",

journal = "Journal of Pain and Symptom Management",

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TY - JOUR

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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KW - statistical methods

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Symptom clusters in advanced cancer patients: An empirical comparison of statistical methods and the impact on quality of life

Abstract

Bibliographical note

Keywords

UN SDGs

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