Validation of the Functional Assessment of Cancer Therapy-General (FACT-G) scale for measuring the health-related quality of life in Korean women with breast cancer.
01 Jul 2004-Japanese Journal of Clinical Oncology (Oxford University Press)-Vol. 34, Iss: 7, pp 393-399
TL;DR: The Korean version of the FACT-G scale was demonstrated as reliable and valid and can be used in research and clinical settings to assess the quality of life of Korean breast cancer patients.
Abstract: Background: The Functional Assessment of Cancer Therapy-General (FACT-G) scale, which was developed and validated in the USA, is widely used to measure the health-related quality of life in cancer patients. The purpose of the present study was to empirically validate the FACT-G scale with Korean breast cancer patients. Methods: A convenience sample of 193 women with breast cancer was recruited from a university hospital. The subjects were asked to complete the Korean version of the FACT-G scale. The data were analyzed using exploratory factor analysis with varimax rotation to determine factor construct validity. The loading criterion was set at 0.40 and above, inter-subscale correlations were computed using Pearson correlation, and the reliability of the internal consistency for the total scale and its subscales were assessed by Cronbach’s alpha. Results: The factor structure of the Korean version of the FACT-G scale paralleled that of the English version: the physical, social/family, emotional, and functional well-being subscales were constructively valid in Korean breast cancer patients. However, there is the possibility of culture-specific differences in the social/family well-being subscale, and some problematic translations were revealed. Cronbach’s alpha for the total scale was 0.89 and that for the subscales ranged from 0.78 to 0.90. Conclusion: The Korean version of the FACT-G scale was demonstrated as reliable and valid. Therefore, the scale can be used in research and clinical settings to assess the quality of life of Korean breast cancer patients.
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TL;DR: Quality of life data provided scientific evidence for clinical decision-making and conveyed helpful information concerning breast cancer patients' experiences during the course of the disease diagnosis, treatment, disease-free survival time, and recurrences; otherwise finding patient-centered solutions for evidence-based selection of optimal treatments, psychosocial interventions, patient-physician communications, allocation of resources, and indicating research priorities were impossible.
Abstract: Quality of life in patients with breast cancer is an important outcome. This paper presents an extensive overview on the topic ranging from descriptive findings to clinical trials. This was a bibliographic review of the literature covering all full publications that appeared in English language biomedical journals between 1974 and 2007. The search strategy included a combination of key words 'quality of life' and 'breast cancer' or 'breast carcinoma' in titles. A total of 971 citations were identified and after exclusion of duplicates, the abstracts of 606 citations were reviewed. Of these, meetings abstracts, editorials, brief commentaries, letters, errata and dissertation abstracts and papers that appeared online and were indexed ahead of publication were also excluded. The remaining 477 papers were examined. The major findings are summarized and presented under several headings: instruments used, validation studies, measurement issues, surgical treatment, systemic therapies, quality of life as predictor of survival, psychological distress, supportive care, symptoms and sexual functioning. Instruments-Several valid instruments were used to measure quality of life in breast cancer patients. The European Organization for Research and Treatment of Cancer Core Cancer Quality of Life Questionnaire (EORTC QLQ-C30) and its breast cancer specific complementary measure (EORTC QLQ-BR23) and the Functional Assessment Chronic Illness Therapy General questionnaire (FACIT-G) and its breast cancer module (FACIT-B) were found to be the most common and well developed instruments to measure quality of life in breast cancer patients. Surgery-different surgical procedures led to relatively similar results in terms of quality of life assessments, although mastectomy patients compared to conserving surgery patients usually reported a lower body image and sexual functioning. Systemic therapies-almost all studies indicated that breast cancer patients receiving chemotherapy might experience several side-effects and symptoms that negatively affect their quality of life. Adjuvant hormonal therapies also were found to have similar negative impact on quality of life, although in general they were associated with improved survival. Quality of life as predictor of survival-similar to known medical factors, quality of life data in metastatic breast cancer patients was found to be prognostic and predictive of survival time. Psychological distress-anxiety and depression were found to be common among breast cancer patients even years after the disease diagnosis and treatment. Psychological factors also were found to predict subsequent quality of life or even overall survival in breast cancer patients. Supportive care-clinical treatments to control emesis, or interventions such as counseling, providing social support and exercise could improve quality of life. Symptoms-Pain, fatigue, arm morbidity and postmenopausal symptoms were among the most common symptoms reported by breast cancer patients. As recommended, recognition and management of these symptoms is an important issue since such symptoms impair health-related quality of life. Sexual functioning-breast cancer patients especially younger patients suffer from poor sexual functioning that negatively affect quality of life. There was quite an extensive body of the literature on quality of life in breast cancer patients. These papers have made a considerable contribution to improving breast cancer care, although their exact benefit was hard to define. However, quality of life data provided scientific evidence for clinical decision-making and conveyed helpful information concerning breast cancer patients' experiences during the course of the disease diagnosis, treatment, disease-free survival time, and recurrences; otherwise finding patient-centered solutions for evidence-based selection of optimal treatments, psychosocial interventions, patient-physician communications, allocation of resources, and indicating research priorities were impossible. It seems that more qualitative research is needed for a better understanding of the topic. In addition, issues related to the disease, its treatment side effects and symptoms, and sexual functioning should receive more attention when studying quality of life in breast cancer patients.
696 citations
Cites background or methods from "Validation of the Functional Assess..."
...A few studies reported translation and validation findings for the instruments used to assess quality of life among breast cancer patients in different cultures (for example see [48,54,56])....
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...[54] 2004 The Functional Assessment of Cancer Therapy-General (FACT-G) Validation of the Korean version...
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TL;DR: The results of this study indicate that Bojungikki-tang may have beneficial effects on cancer-related fatigue and quality of lives in cancer patients.
Abstract: Background: Bojungikki-tang (Bu-Zhong-Yi-Qi-Tang in Chinese or Hochu-ekki-to in Japanese) is a widely used herbal prescription in traditional medicine in China, Japan, and Korea. The aim of this st...
122 citations
Cites methods from "Validation of the Functional Assess..."
...The Korean version of FACT-G was validated by Lee et al.(16) A Korean version of the additional 13 items for FACT-F was developed by translation of 2 separate translators followed by internal validation based on patient group feedback....
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...The Korean version of FACT-G was validated by Lee et al.16 A Korean version of the additional 13 items for FACT-F was developed by translation of 2 separate translators followed by internal validation based on patient group feedback....
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TL;DR: The FACT-G and its subscales demonstrated acceptable reliability evidence across observed studies, without substantial variability due to scale or demographic characteristics.
Abstract: The aim of this study was to conduct a reliability generalization of the Functional Assessment of Cancer Therapy-General (FACT-G) and its subscales to examine variation in score reliability across all published studies. We reviewed 344 publications based on predetermined criteria. About 78 published studies reported Cronbach’s Alpha reliability coefficients from their study in which data were collected. Sample size based weights were applied, and studies were coded on several scale and demographic characteristics. Using independent samples t tests, we examined associations between study characteristics and internal consistency variability. Average FACT-G score reliability was .88 (subscales ranged between .71–.83). Three variables produced small, statistically significant (P ≤ .05) eta squared effects (ranging between .06–.21) due to different sources of variation in the FACT-G and subscales: ethnicity, cancer type, and study type-all of which appeared to be related to disproportionate representation of studies with the majority including Caucasian samples, mixed cancer samples, and validation type studies. The FACT-G and its subscales demonstrated acceptable reliability evidence across observed studies, without substantial variability due to scale or demographic characteristics.
118 citations
TL;DR: The aims of the study were to describe PTG in patients with hepatobiliary carcinoma, examine agreement between the patient and caregiver measures of patient PTG, and test the associations between PTG and other psychological factors and clinically relevant outcomes.
Abstract: Objective: The study of posttraumatic growth (PTG) has burgeoned over the last decade, particularly in the area of oncology. The aims of the study were to: (1) describe PTG in patients with hepatobiliary carcinoma, (2) examine agreement between the patient and caregiver measures of patient PTG, and (3) test the associations between PTG and other psychological factors and clinically relevant outcomes.
Methods: Two hundred and two patients with hepatobiliary carcinoma completed a battery of standardized questionnaires that measured PTG, depressive symptoms, optimism, expressed emotion, and quality of life (QOL). A subsample of family caregivers also completed ratings of patient PTG, using the Posttraumatic Growth Inventory (PTGI), as well as their own PTG.
Results: No significant increase in the patients' PTG was observed between diagnosis and 6-month follow-up with the exception of the Relating to Others subscale of the PTGI. PTG was not found to be associated with QOL or depressive symptoms. At diagnosis, the agreement between the patients' PTG and family caregivers' rating of patient PTG was found to be high (ICC=0.34–0.74, p=0.001–0.05). PTG was found to be significantly associated with optimism (r=0.20 p=0.02–0.05) and traumatic life events reported in the past 3 years, including recent losses (F(1, 52)=6.0, p=0.02) and severe physical injury (F(1, 52)=5.5, p=0.02). Caregivers reported PTG as a result of their loved one's diagnosis of cancer.
Conclusion: Preliminary results suggest that PTG is relatively stable over the first 6 months after diagnosis and changes as a result of a diagnosis of cancer were reported, and possibly observed, by others. Family caregivers also experience PTG as a result of their loved one's diagnosis of advanced cancer. Copyright © 2010 John Wiley & Sons, Ltd.
88 citations
TL;DR: Probiotics improved bowel symptoms and quality of life in colorectal cancer survivors in a double-blind, randomized, placebo-controlled trial.
75 citations
References
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01 Jan 1983
TL;DR: In this Section: 1. Multivariate Statistics: Why? and 2. A Guide to Statistical Techniques: Using the Book Research Questions and Associated Techniques.
Abstract: In this Section: 1. Brief Table of Contents 2. Full Table of Contents 1. BRIEF TABLE OF CONTENTS Chapter 1 Introduction Chapter 2 A Guide to Statistical Techniques: Using the Book Chapter 3 Review of Univariate and Bivariate Statistics Chapter 4 Cleaning Up Your Act: Screening Data Prior to Analysis Chapter 5 Multiple Regression Chapter 6 Analysis of Covariance Chapter 7 Multivariate Analysis of Variance and Covariance Chapter 8 Profile Analysis: The Multivariate Approach to Repeated Measures Chapter 9 Discriminant Analysis Chapter 10 Logistic Regression Chapter 11 Survival/Failure Analysis Chapter 12 Canonical Correlation Chapter 13 Principal Components and Factor Analysis Chapter 14 Structural Equation Modeling Chapter 15 Multilevel Linear Modeling Chapter 16 Multiway Frequency Analysis 2. FULL TABLE OF CONTENTS Chapter 1: Introduction Multivariate Statistics: Why? Some Useful Definitions Linear Combinations of Variables Number and Nature of Variables to Include Statistical Power Data Appropriate for Multivariate Statistics Organization of the Book Chapter 2: A Guide to Statistical Techniques: Using the Book Research Questions and Associated Techniques Some Further Comparisons A Decision Tree Technique Chapters Preliminary Check of the Data Chapter 3: Review of Univariate and Bivariate Statistics Hypothesis Testing Analysis of Variance Parameter Estimation Effect Size Bivariate Statistics: Correlation and Regression. Chi-Square Analysis Chapter 4: Cleaning Up Your Act: Screening Data Prior to Analysis Important Issues in Data Screening Complete Examples of Data Screening Chapter 5: Multiple Regression General Purpose and Description Kinds of Research Questions Limitations to Regression Analyses Fundamental Equations for Multiple Regression Major Types of Multiple Regression Some Important Issues. Complete Examples of Regression Analysis Comparison of Programs Chapter 6: Analysis of Covariance General Purpose and Description Kinds of Research Questions Limitations to Analysis of Covariance Fundamental Equations for Analysis of Covariance Some Important Issues Complete Example of Analysis of Covariance Comparison of Programs Chapter 7: Multivariate Analysis of Variance and Covariance General Purpose and Description Kinds of Research Questions Limitations to Multivariate Analysis of Variance and Covariance Fundamental Equations for Multivariate Analysis of Variance and Covariance Some Important Issues Complete Examples of Multivariate Analysis of Variance and Covariance Comparison of Programs Chapter 8: Profile Analysis: The Multivariate Approach to Repeated Measures General Purpose and Description Kinds of Research Questions Limitations to Profile Analysis Fundamental Equations for Profile Analysis Some Important Issues Complete Examples of Profile Analysis Comparison of Programs Chapter 9: Discriminant Analysis General Purpose and Description Kinds of Research Questions Limitations to Discriminant Analysis Fundamental Equations for Discriminant Analysis Types of Discriminant Analysis Some Important Issues Comparison of Programs Chapter 10: Logistic Regression General Purpose and Description Kinds of Research Questions Limitations to Logistic Regression Analysis Fundamental Equations for Logistic Regression Types of Logistic Regression Some Important Issues Complete Examples of Logistic Regression Comparison of Programs Chapter 11: Survival/Failure Analysis General Purpose and Description Kinds of Research Questions Limitations to Survival Analysis Fundamental Equations for Survival Analysis Types of Survival Analysis Some Important Issues Complete Example of Survival Analysis Comparison of Programs Chapter 12: Canonical Correlation General Purpose and Description Kinds of Research Questions Limitations Fundamental Equations for Canonical Correlation Some Important Issues Complete Example of Canonical Correlation Comparison of Programs Chapter 13: Principal Components and Factor Analysis General Purpose and Description Kinds of Research Questions Limitations Fundamental Equations for Factor Analysis Major Types of Factor Analysis Some Important Issues Complete Example of FA Comparison of Programs Chapter 14: Structural Equation Modeling General Purpose and Description Kinds of Research Questions Limitations to Structural Equation Modeling Fundamental Equations for Structural Equations Modeling Some Important Issues Complete Examples of Structural Equation Modeling Analysis. Comparison of Programs Chapter 15: Multilevel Linear Modeling General Purpose and Description Kinds of Research Questions Limitations to Multilevel Linear Modeling Fundamental Equations Types of MLM Some Important Issues Complete Example of MLM Comparison of Programs Chapter 16: Multiway Frequency Analysis General Purpose and Description Kinds of Research Questions Limitations to Multiway Frequency Analysis Fundamental Equations for Multiway Frequency Analysis Some Important Issues Complete Example of Multiway Frequency Analysis Comparison of Programs
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TL;DR: In this article, an index of factorial simplicity, employing the quartimax transformational criteria of Carroll, Wrigley and Neuhaus, and Saunders, was developed.
Abstract: An index of factorial simplicity, employing the quartimax transformational criteria of Carroll, Wrigley and Neuhaus, and Saunders, is developed. This index is both for each row separately and for a factor pattern matrix as a whole. The index varies between zero and one. The problem of calibrating the index is discussed.
10,346 citations
"Validation of the Functional Assess..." refers methods in this paper
...The Kaiser–Meyer–Olkin (KMO) test and Bartlett’s test of sphericity were performed to justify the suitability of data for a factor analysis ( 8 ,9)....
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...The value of KMO’s measure of sampling adequacy was 0.87, which is meritorious for a factor analysis ( 8 )....
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TL;DR: The FACT-G meets or exceeds all requirements for use in oncology clinical trials, including ease of administration, brevity, reliability, validity, and responsiveness to clinical change.
Abstract: PURPOSEWe developed and validated a brief, yet sensitive, 33-item general cancer quality-of-life (QL) measure for evaluating patients receiving cancer treatment, called the Functional Assessment of Cancer Therapy (FACT) scale.METHODS AND RESULTSThe five-phase validation process involved 854 patients with cancer and 15 oncology specialists. The initial pool of 370 overlapping items for breast, lung, and colorectal cancer was generated by open-ended interview with patients experienced with the symptoms of cancer and oncology professionals. Using preselected criteria, items were reduced to a 38-item general version. Factor and scaling analyses of these 38 items on 545 patients with mixed cancer diagnoses resulted in the 28-item FACT-general (FACT-G, version 2). In addition to a total score, this version produces subscale scores for physical, functional, social, and emotional well-being, as well as satisfaction with the treatment relationship. Coefficients of reliability and validity were uniformly high. The ...
5,232 citations
Book•
21 Mar 2003
TL;DR: In this article, the authors present an overview of Factor Analysis in Health Care Research Decision-Making Process in Exploratory Factor Analysis and compare the two-factor solution using PCA and PAF.
Abstract: 1. An Overview of Factor Analysis Characteristics of Factor Analysis Exploratory Vs. Confirmatory Factor Analysis Assumptions of Exploratory Factor Analysis Historical Developments of Factor Analysis Uses of Factor Analysis in Health Care Research Decision-Making Process in Exploratory Factor Analysis 2. Designing and Testing the Instrument Types of Measurement and Frameworks The Use of Latent Variables in Instrument Development Identifying Empirical Indicators of Latent Variables Using Qualitative Research Methods to Identify Empirical Indicators Additional Qualitative Approaches to Identifying Empirical Indicators Development of the Instrument Scoring the Instrument Pilot Testing the Instrument Determining the Number of Subjects 3. Assessing the Characteristics of Matrices Characteristics and Types of Matrices Tests of Matrices Review of the Process 4. Extracting the Initial Factors Evaluating the Correlation Matrix Sources of Variance in Factor Analysis Models Determining the Factor Extraction Method Selecting the Number of Factors to Retain Comparing the Two-Factor Solution Using PCA and PAF 5. Rotating the Factors Achieving a Simple Structure Types of Rotations Mapping Factors in Geometric Space Orthogonal Rotations Oblique Rotations Comparing the Orthogonal and Oblique Solutions Advantages and Disadvantages of the Oblique Solution Choosing Between Orthogonal and Oblique Rotations Summary of the Process of Rotations 6. Evaluating and Refining the Factors Evaluating and Refining the Factors Assessing the Reliability of an Instrument Evaluating the Internal Consistency of an Instrument Estimating the Effects on Reliability of Increasing or Decreasing Items Cronbach's Coefficient Alpha Assessing Reliability Using Cronbach's Alpha: A Computer Example Two Additional Reliability Estimates: Temporal Stability and Equivalence 7. Interpreting Factors and Generating Factor Scores Interpreting the Factors Naming the Factors Interpreting and Naming the Four Factors on the CGTS Scale Determining Composite Factor Scores 8. Reporting and Replicating the Results When to Report the Results What to Include in the Report An Exemplar of a Published Report Replicating the Factors in Other Studies Conclusions Appendix A: Concerns About Genetic Testing Scale Appendix B: SAS Commands and Generate Output Appendix C: Output for 20-item CGTS Scale Appendix D: Tables for the Chi-Square and Normal Distributions Appendix E: Unraveling the Mystery of Principal Component Extraction References Index About the Authors
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Book•
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TL;DR: In this paper, the authors developed and tested a questionnaire for clinical trials and found that the questionnaire scores and measures were validated, reliability, and sensitivity, respectively, in terms of Validity, Reliability, Sensitivity.
Abstract: Principles of Measurement Scales. DEVELOPING AND TESTING QUESTIONNAIRES. Scores and Measurements: Validity, Reliability, Sensitivity. Multi-item Scales. Factor Analysis. Item Response Theory and Differential Item Functioning. Questionnaire Development and Scoring. ANALYSIS OF QoL DATA. Cross-sectional Analysis. Exploring Longitudinal Data. Modelling Longitudinal Data. Missing Data. Quality-adjusted Survival. PRACTICAL ASPECTS AND CLINICAL INTERPRETATION. Clinical Trials. Sample Sizes. Practical and Reporting Issues. Clinical Interpretation. Appendices. References. Index.
1,542 citations