Alternative analytical methods for the identification of cancer related symptom clusters

Queensland University of Technology School of Nursing and Midwifery Faculty of Health Institute of Health and Biomedical Innovation Alternative Analytical Methods for the Identification of Cancer-Related Symptom Clusters Helen Mary Skerman DipTeach, BSc, GradDipCompEd, MSocSc (App) This thesis is submitted to fulfil the requirements for the Award of Doctor of Philosophy MAY 2010 KEY WORDS Symptom clusters; symptoms; cancer; symptom experience; symptom management strategies; literature review; empirical methods; multivariate methods; exploratory factor analysis; common factor analysis; cluster analysis; principal axis factoring; stability; longitudinal analysis; chemotherapy; outpatients; nursing research; oncology; Theory of Unpleasant Symptoms i ABSTRACT Advances in symptom management strategies through a better understanding of cancer symptom clusters depend on the identification of symptom clusters that are valid and reliable The purpose of this exploratory research was to investigate alternative analytical approaches to identify symptom clusters for patients with cancer, using readily accessible statistical methods, and to justify which methods of identification may be appropriate for this context Three studies were undertaken: (1) a systematic review of the literature, to identify analytical methods commonly used for symptom cluster identification for cancer patients; (2) a secondary data analysis to identify symptom clusters and compare alternative methods, as a guide to best practice approaches in cross-sectional studies; and (3) a secondary data analysis to investigate the stability of symptom clusters over time The systematic literature review identified, in 10 years prior to March 2007, 13 cross-sectional studies implementing multivariate methods to identify cancer related symptom clusters The methods commonly used to group symptoms were exploratory factor analysis, hierarchical cluster analysis and principal components analysis Common factor analysis methods were recommended as the best practice cross-sectional methods for cancer symptom cluster identification A comparison of alternative common factor analysis methods was conducted, in a secondary analysis of a sample of 219 ambulatory cancer patients with mixed diagnoses, assessed within one month of commencing chemotherapy treatment Principal axis factoring, unweighted least squares and image factor analysis ii identified five consistent symptom clusters, based on patient self-reported distress ratings of 42 physical symptoms Extraction of an additional cluster was necessary when using alpha factor analysis to determine clinically relevant symptom clusters The recommended approaches for symptom cluster identification using nonmultivariate normal data were: principal axis factoring or unweighted least squares for factor extraction, followed by oblique rotation; and use of the scree plot and Minimum Average Partial procedure to determine the number of factors In contrast to other studies which typically interpret pattern coefficients alone, in these studies symptom clusters were determined on the basis of structure coefficients This approach was adopted for the stability of the results as structure coefficients are correlations between factors and symptoms unaffected by the correlations between factors Symptoms could be associated with multiple clusters as a foundation for investigating potential interventions The stability of these five symptom clusters was investigated in separate common factor analyses, and 12 months after chemotherapy commenced Five qualitatively consistent symptom (Musculoskeletal-discomforts/lethargy, clusters were identified Oral-discomforts, over time Gastrointestinal- discomforts, Vasomotor-symptoms, Gastrointestinal-toxicities), but at 12 months two additional clusters were determined (Lethargy and Gastrointestinal/digestive symptoms) Future studies should include physical, psychological, and cognitive symptoms Further investigation of the identified symptom clusters is required for validation, to examine causality, and potentially to suggest interventions for symptom management Future studies should use longitudinal analyses to investigate iii change in symptom clusters, the influence of patient related factors, and the impact on outcomes (e.g., daily functioning) over time iv TABLE OF CONTENTS Keywords i Abstract ii Table of Contents v List of Tables x List of Figures xi Declaration of Authorship xii Glossary of Acronyms and Terms xiii Publications from the Research Program xv Statement of Contribution of Co-authors xvi Funding for the Research Program xvii Acknowledgements xviii Chapter 1: Introduction 1.1 Introduction 1.2 The Burden of Cancer 1.3 Rationale and Significance of the Research 1.4 Research Purpose and Objectives 1.5 Research Questions 1.6 Thesis Outline v Chapter 2: Background 2.1 Introduction 13 2.2 The Cancer Symptom Experience 13 2.2.1 Change in the Symptom Experience 14 2.2.2 Change in the Symptom Experience Over Time 15 2.3 Concept of a Symptom Cluster 16 2.4 The Clinical Relevance of Symptom Clusters 19 2.5 The Symptom Experience and Symptom Management Models 21 2.5.1 The Theory of Unpleasant Symptoms (TOUS) 22 2.5.2 The Symptoms Experience Model (SEM) 23 2.5.3 The Model of Symptom Management 24 2.5.4 Symptom Interaction Framework 25 2.6 Summary 25 Chapter 3: Symptom Cluster Identification 3.1 Introduction 27 3.2 Measuring Symptoms 27 3.3 Approaches to Symptom Cluster Identification 31 3.3.1 The Clinical Approach 31 3.3.2 The Empirical Identification of Symptom Clusters 32 3.4 Towards Best Practice Methods 36 3.5 Summary 38 vi Chapter 4: A Systematic Literature Review 4.1 Introduction 41 4.2 Method 42 4.3 Multivariate Methods to Identify Cancer-Related Symptom Clusters 43 4.4 Summary 83 Chapter 5: Methods 5.1 Introduction 85 5.2 The Conceptual Framework 86 5.3 Study Design 90 5.3.1 The Parent Study 5.4 Measures 5.5 90 94 5.4.1 Measures in Parent Study - Ambulatory Care Project 94 5.4.2 Selected Measures in Current Study 96 Statistical Analysis 99 5.5.1 Data Quality 99 5.5.2 Missing Data 100 5.5.3 Complete Data for Exploratory Factor Analysis 102 5.5.4 Study 2: Identification of Symptom Clusters 102 5.5.5 Number of Factors to Retain 104 5.5.6 Simple Structure and Symptom Cluster Identification 106 5.5.7 Alternative Methods of Common Factor Extraction 107 5.5.8 Study 3: Identification of Symptom Clusters over Time 110 5.6 Sample size 112 5.7 Limitations 112 vii Chapter 6: The Empirical Identification of Symptom Clusters 6.1 Introduction 115 6.2 Identification of Cancer-Related Symptom Clusters: An Empirical 116 Comparison of Exploratory Factor Analysis Methods 6.3 Summary 145 Chapter 7: The Empirical Identification of Symptom Clusters over Time 7.1 Introduction 147 7.2 Cancer-Related Symptom Clusters, and 12 Months after Commencing Chemotherapy: An Empirical Investigation 148 Chapter 8: Final Discussion and Conclusions 177 8.1 Key Findings 178 8.1.1 Multivariate Methods for Symptom Cluster Identification 180 8.1.2 EFA Decisions for Symptom Cluster Identification 181 8.1.3 Stability of Symptom Clusters Identified at Different Times 185 8.2 Strengths and Limitations 187 8.3 Implications of the Findings 195 8.3.1 Conceptual Implications 195 8.3.2 Analytical Implications 199 8.3.3 Implications for Clinical Practice 203 8.3.4 Implications for Future Research 204 8.4 Conclusion viii 206 Given, B., Given, C., Azzouz, F., & Stommel, M (2001) Physical functioning of elderly cancer patients prior to diagnosis and following initial treatment Nurs Res, 50(4), 222-232 Given, C W., Given, B., Azzouz, F., Kozachik, S., & Stommel, M (2001) Predictors of pain and fatigue in the year following diagnosis among elderly cancer patients Journal of Pain and Symptom Management, 21(6), 456-466 Given, C W., Stommel, M., Given, B., Osuch, J., Kurtz, M E., & Kurtz, J C (1993) The influence of cancer patients' symptoms and functional states 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The conceptual framework for the identification of symptom clusters was the Theory of Unpleasant Symptoms (Lenz et al., 1997), which incorporates the concept of multiple symptoms that occur simultaneously... guidance for the most effective analytic methods for identifying valid and reliable symptom clusters, to support the advancement of symptom management in oncology 1.2 The Burden of Cancer In the developed