- Email: [email protected]
Measuring the Symptom Experience of Chinese Cancer Patients: A Validation of the Chinese Version of the Memorial Symptom Assessment Scale
44
Journal of Pain and Symptom Management
Vol. 37 No. 1 January 2009
Original Article
Measuring the Symptom Experience of Chinese Cancer Patients: A Validation of the Chinese Version of the Memorial Symptom Assessment Scale Karis K.F. Cheng, RN, PhD, PDip Epidemiol&Biostat, Eric M.C. Wong, MA, W.M. Ling, RN, MN, Carmen W.H. Chan, RN, PhD, and David R. Thompson, RN, PhD The Nethersole School of Nursing (K.K.F.C., C.W.H.C.), and Division of Biostatistics, School of Public Health (E.M.C.W.), The Chinese University of Hong Kong, Hong Kong; Department of Clinical Oncology (W.M.L.), Pamela Youde Nethersole Eastern Hospital, Hong Kong; and Departments of Health Sciences and Cardiovascular Sciences (D.R.T.), University of Leicester, Leicester, United Kingdom
Abstract The purpose of this study was to translate the Memorial Symptom Assessment Scale (MSAS) into Chinese and evaluate the psychometric properties of this version. The original MSAS is a 32-item, patient-rated measure that was developed to assess common cancer-related physical and psychological symptoms with respect to frequency, intensity, and distress. In this study, a two-phase design was used. Phase I involved iterative forwardebackward translation, testing of content validity (CVI) and a pretest. Phase II established the psychometric properties of the Chinese version MSAS (MSAS-Ch). Results showed that the MSAS-Ch achieved content relevancy CVI of 0.94 and semantic equivalence CVI of 0.94. Pretesting was performed in 10 cancer patients, and the results revealed adequate content coverage and comprehensibility of the MSAS-Ch. A convenience sample of 370 patients undergoing cancer therapy or at the early post-treatment stage was recruited for psychometric evaluation. Confirmatory factor analysis confirmed the construct validity of the MSAS-Ch, with a good fit between the factor structure of the original version and our present sample data (goodnessof-fit indices all above 0.95). The internal consistency reliability of subscales and total MSAS-Ch was moderately high, with Cronbach alpha coefficients ranging from 0.79 to 0.87. The testeretest intraclass correlation results for the subscale and total MSAS-Ch ranged from 0.68 to 0.79. The subscale scores of MSAS-Ch were moderately correlated with the scores on various validation measurements that assessed psychological distress, pain, and healthrelated quality of life (r ¼ 0.46e0.65, P < 0.01), confirming that they were measurements of similar constructs. The validity of the construct validity was also supported by comparing the MSAS-Ch scores for subpopulations that varied clinically. Inpatients and patients with poorer performance status scored higher on the MSAS-Ch subscale and total scores than outpatients and patients with higher performance status (P < 0.05). Our study shows that
This study was partially supported by the Lee Hysan Foundation Fund, United College of the Chinese University of Hong Kong. Address correspondence to: Karis Kin-Fong Cheng, RN, PhD, Room 732, Esther Lee Building, Chung Chi Ó 2009 U.S. Cancer Pain Relief Committee Published by Elsevier Inc. All rights reserved.
College, The Nethersole School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong. E-mail: [email protected] Accepted for publication: December 28, 2007. 0885-3924/09/$esee front matter doi:10.1016/j.jpainsymman.2007.12.019
Vol. 37 No. 1 January 2009
Validation of the Chinese Version of the MSAS
45
the MSAS-Ch has adequate psychometric properties of validity and reliability, and can be used to assess symptoms during cancer therapy and at the early post-treatment stage in Chinese-speaking patients. J Pain Symptom Manage 2009;37:44e57. Ó 2009 U.S. Cancer Pain Relief Committee. Published by Elsevier Inc. All rights reserved. Key Words Symptoms, cancer, MSAS, validation
Introduction Patients with cancer suffer from a multitude of intense physical and psychological symptoms regardless of the stage of the disease.1,2 Some symptoms are related to progression of the disease, whereas others are associated with the early and late effects of cancer treatment, or to problems with psychosocial adjustment.3,4 Symptoms seldom occur in isolation in patients with cancer.5 Previous studies have demonstrated that symptoms, such as pain, depression, fatigue, and others, occur in clusters and can influence the occurrence of other symptoms. The concept of symptom clusters is becoming increasingly recognized as an important platform for symptom management for cancer patients.5,6 In fact, many surveys have documented a high prevalence of the grouping of symptoms in cancer patients, with a median number ranging from eight to 11 symptoms per patient.3,4,7e9 Efforts to describe symptoms have shown that each symptom has an associated constellation of shared dimensions, including frequency, intensity, and level of perceived distress.10 Health care professionals experienced in oncology care know that the morbidity of cancer-related symptoms can be profound and very distressing to patients. In addition to compromising the functional capacity of the patient, cancer-related symptoms can significantly increase psychological distress and alter the patient’s health-related quality of life (HRQoL).3,8 The resulting morbidity and its impact on HRQoL can even affect the patient’s decision to continue with treatment, with an obvious impact on the prospects of surviving the disease.11 During the past decade, there has been a substantial increase in the concern and attention given to symptom control and HRQoL of patients with a variety of malignancies and
disease trajectories. The literature has repeatedly highlighted that comprehensive symptom assessment is a prerequisite for good symptom control and HRQoL improvement.2,3,8 Assessment and measurement of cancer-related symptoms is important at all stages of the illness.9 At present, few studies have been undertaken that systematically assess the prevalence and burden of cancer-related symptoms. Most of the published studies refer to the symptom experience in patients at the end of life,9,12e15 while ignoring their experience during diagnosis, treatment, and disease remission. Little is known about the prevalence, severity and distress caused by symptoms in the Chinese cancer population. One of the barriers to conducting such studies has been the lack of validated comprehensive symptom assessment tools. The Memorial Symptom Assessment Scale (MSAS) is one of the few available comprehensive cancer-related symptom assessment tools.2,3 The format of the MSAS instrument allows for more detailed analyses of symptom severity and distress, in addition to estimates of symptom prevalence. The original development and validation of the MSAS involved item generation and scale construction, initial evaluation and re-evaluation of factors, reliability and validity, and the differentiation of various clinical populations. The overall validation results were good. Initial and subsequent factor analysis of the MSAS items resulted in a two-factor solution that supports the meaningful groupings of symptoms.2,16 The reported correlations with the Functional Living Index-Cancer were 0.60 to 0.78, and with the Karnofsky Performance Status Scale (KPS) were 0.31 to 0.58.2 The correlation with the FACT-G Sum Quality of Life was 0.65, with subscale correlation coefficients ranging from 0.64 to 0.76.3 Cronbach alpha for the MSAS subscales ranged from 0.82 to 0.88.2,3 Test-retest reliability for the whole scale
46
Cheng et al.
within 24e48 hours was 0.70.16 The aim of this study was to translate the MSAS into Chinese and determine whether the factor structure, and the reliability and validity of the translated MSAS, are suitable for use with Chinese cancer patients.
Methods Sample The study was conducted in a regional hospital in Hong Kong, with the approval of the Institutional Review Board. The subjects recruited were over 18 years of age, had been diagnosed with solid tumors, and were able to understand the study and give informed consent. They were being treated with chemotherapy or radiotherapy, or were in the first 12 months of the post-treatment stage. Patients with a medical diagnosis of encephalopathy or a psychiatric disease that could make evaluation difficult were excluded from the study. Two groups of subjects were recruited for this study. The subjects in Sample 1 were involved in the pretesting. The subjects in Sample 2 were involved in the testing of the main psychometric properties of the Chinese version MSAS (MSAS-Ch). The study was conducted in accordance with the pertinent sections of the Declaration of Helsinki. All participants gave their written informed consent before being enrolled in the study. The enrolled subjects were given a packet of questionnaires, which included the MSAS-Ch, the Chinese version of the Functional Assessment of Cancer Therapy-General (FACT-G [Ch]),17 the Chinese version of the Symptom Distress Scale (SDS-C)18 and, if they had pain, the Chinese version of the Brief Pain Inventory Short Form (BPI-C).19 All of the instruments were completed on the same day in a random order. Observer ratings of KPS were also gathered.20
Instruments MSAS. The MSAS is a multidimensional instrument that evaluates 32 common physical and psychological symptoms. Twenty-four symptoms are evaluated with respect to frequency, intensity, and distress, and eight symptoms are evaluated in terms of severity and distress.2 In the MSAS, each symptom is recorded as present or absent, and if present, is
Vol. 37 No. 1 January 2009
rated using a four- or five-point rating scale for frequency, severity, and associated distress during the previous seven days, with higher scores indicating greater frequency, more severity, and higher distress. If a symptom is absent, each dimension is scored as 0 and the score for that symptom is 0. If a symptom is present, the symptom score is an average of its dimensions. The scoring of the MSAS yields several subscale scores, including a Physical Symptom subscale score (PHYS), a Psychological Symptom subscale score (PSYCH), and a Global Distress Index (GDI). The PHYS is the average of the score for the 12 symptoms: lack of appetite, lack of energy, pain, feeling drowsy, constipation, dry mouth, nausea, vomiting, change in food taste, weight loss, feeling bloated, and dizziness. The PSYCH is the average of the score for the six symptoms: worrying, feeling sad, feeling nervous, difficulty sleeping, feeling irritable, and difficulty concentrating. The GDI is the average of the frequency of four psychological symptoms (feeling sad, worrying, feeling irritable, feeling nervous) and the distress associated with six physical symptoms (lack of appetite, lack of energy, pain, feeling drowsy, constipation, dry mouth). The Total MSAS score (TMSAS) is the average of the symptom scores of all 32 symptoms in the MSAS.2 FACT-G (Ch). The FACT-G (Ch) is a 29-item general measure of HRQoL for Chinese cancer patients. Each item is scored from 0 to 4, anchored from ‘‘not at all’’ to ‘‘very much.’’ The FACT-G (Ch) has five subscales: physical well-being (Phy) (7 items), social/family wellbeing (Soc/Fam) (7 items), relationship with physician (Doc) (2 items), emotional wellbeing (Emt) (5 items), and functional well-being (Fnt) (7 items), with the total FACT-G (Ch) score ranging from 0 to 116. A lower score indicates a poorer level of HRQoL.17 SDS-C. The SDS-C provides a valid measure of global symptom distress in Chinese cancer patients. The SDS-C is a 13-item scale evaluating 11 symptoms. Symptoms are scored from 1 ‘‘no distress’’ to 5 ‘‘extreme distress.’’ The SDS-C total score is the sum of the scores for all 13 items.18 BPI-C. The BPI-C is an 11-item instrument for Chinese cancer patients to assess the
Vol. 37 No. 1 January 2009
Validation of the Chinese Version of the MSAS
multi-dimensional nature of pain, including intensity of pain (four single items) and subsequent interference with life activities (seven items) in the preceding 24 hours. Each item is rated on a scale of 0 (‘‘no pain’’) to 10 (‘‘the worst I can imagine’’).19 For this study, a single item of worst pain was used to yield the severity score. An interference score, representing the average of the seven items, was computed.
Validation Procedures There were two phases in the validation procedures. In the first, forward and backward translations were undertaken, as well as expert evaluation of the content relevance and semantic equivalence and a pre-testing. In the second, psychometric evaluation was performed. Phase I. The translation of the MSAS into Chinese involved a linguistics expert and three bilingual investigators. In the first place, an independent forward translation of the original English version of the MSAS into Chinese was performed by a linguistics expert and a bilingual investigator, to make the wording and expressions of the items culturally accessible to people in the Hong Kong community. The next step was to make a back-translation into English, using two other bilingual investigators who had not seen the original English version. The forward and backward translations were reviewed and reconciled until an acceptable Chinese translation was reached that best reflected the linguistic and conceptual matter of the original English MSAS. Content validity of the MSAS-Ch was assessed by six experts to rate each item as a relevant element for the target construct within the local culture (content relevance), and to rate each item as conceptually and idiomatically same meaning with the English version (semantic equivalence).21,22 A four-point rating scale was used to assess each of these parameters: 1, not relevant, 2, somewhat relevant, 3, relevant and 4, very relevant. The content validity index (CVI) of content relevance and semantic equivalence for each item and total items were calculated from the proportion of experts who scored items as either 3 or 4.23 Possible CVI scores can range from 0 to 1, and a minimum of 0.80 was adopted for this study.23
47
The MSAS-Ch was pre-tested with 10 cancer patients from an outpatient clinic in an oncology department. All of the subjects were asked to complete the MSAS-Ch and they were then interviewed to determine whether they found the questions to be appropriate and comprehensible. Phase II. In order to determine its psychometric properties, 370 patients from an outpatient clinic, and in the wards in the same oncology department as used for the pre-test, were asked to complete the MSAS-Ch to test its reliability and validity. The statistical analysis began by an assessment of the pattern of missing values among the responses to MSAS-Ch items. A patient was excluded from psychometric evaluation if responses were not given to more than 13% of the items in the MSAS-Ch, as in the validation study of the original MSAS.2 For the purposes of this calculation, severity, frequency and distress were each considered as an item. For patients who had less than 13% of missing items, a simple method for imputing items with reference to the original work was adopted.2 By this method, if at least half of the dimensions in a symptom have been answered, the equation given above for calculating the symptom score is applied, ignoring any dimensions with missing values when making the calculation. Conversely, if more than half of the dimensions in a symptom have not been answered, the symptom score is recorded as missing and thus not included in the calculation of symptom score. The degree of variability among the individual items was assessed using summary statistics. In addition, floor and ceiling effects based on item-descriptive statistics were determined. The floor effect refers to a high percentage of subjects scoring the lowest possible score (not at all), while the ceiling effect refers to a high percentage of subjects achieving the highest possible score (very much).24 A percentage of 20% at the floor or at the ceiling was considered to be a significant effect. Likewise, a percentage of 70% was considered to be a high floor or ceiling, indicating that the scale is limited in its responsiveness to clinical change.24 The individual and total scores for each item were calculated in the same way as in the validation study of the original MSAS. To assess
48
Cheng et al.
structural validity of the MSAS-Ch, confirmatory factor analysis (CFA) using LISREL 8.50 for Windows (Scientific Software International Inc., Lincolnwood, IL) was performed to examine whether the hypothesized factor structure from exploratory factor analysis in the original study adequately fit our present sample data. CFA was used by entering the correlation matrix and using the unweighted least squares estimation procedure.25 The goodness-of-fit indices that we used to assess the fit of each model in this study included (a) the comparative fit index (CFI), (b) the goodness-of-fit index (GFI), (c) the root mean square error of approximation (RMSEA),26 (d) the normed fit index (NFI), (e) the nonnormed fit index (NNFI), and (f) the relative fit index (RFI).27,28 The RMSEA has the advantage of being robust with respect to increase in degree of freedom, and the latter three indices have the advantage of being robust with respect to sample size increases.29 The goodness-of-fit criteria for each index are as follows: CFI, GFI, NFI, NNFI, and RFI >0.9;30 and RMSEA <0.06.31 Cronbach’s alpha coefficient was used to assess the internal consistency reliability. An alpha within the range of 0.70e0.95 was accepted as satisfactory for internal consistency.32,33 For the test-retest reliability testing, 61 hospitalized subjects who had completed the MSAS-Ch were randomly selected and asked to complete the instrument again within 24e48 hours.34,35 The 24e48 hours was judged to be the optimum retest interval; this would be sufficiently long for subjects to forget their initial responses to the items, but not so long that the severity of symptoms would change substantially.34,35 The test-retest analysis of the subscale and total scores was performed using intraclass correlation (ICC). All values greater than 0.70 for ICC were accepted as a satisfactory level for test-retest reliability.36 Concurrent validity was estimated from the correlations between the MSAS-Ch and the FACT-G (Ch) subscales. It was predicted that there would be moderate correlation between MSAS-Ch and relevant FACT-G (Ch) subscales, indicating that they assessed related but different outcome constructs. The convergent validity of the MSAS-Ch was tested by correlating its subscale and total scores with the pain severity and pain interference subscale scores on the
Vol. 37 No. 1 January 2009
BPI-C, as well as with the average total score of the SDS-C. If the correlation coefficient is between 0.4 and 0.8, convergent validity is established.33 Known-group validity was tested by comparing the MSAS-Ch subscale and total scores with the performance status by four different KPS categories (KPS 20e50%; 60e70%; 80%; and 90e100%), and hospitalization status (inpatient and outpatient).
Results Phase I Expert review of the MSAS-Ch was undertaken by two clinical oncologists (33.3%), three oncology nurse specialists (50%), and an advanced practicing nurse in oncology (16.7%). Five of these (83.3%) were female. Results showed that the content relevancy and semantic equivalence CVI for each item ranged from 0.83 to 1. The total 32-item MSAS-Ch had a content relevancy CVI of 0.94 and a semantic equivalence CVI of 0.97, both of which were greater than the predetermined acceptable CVI level of 0.80. A pre-test was conducted with 10 patients (six female) with a mean SD age of 42 13 years (range 21e58 years), and 60% with secondary education. The majority of them were diagnosed with nasopharyngeal cancer or breast cancer (70%). The subjects in the pre-test did not report any significant problem with item comprehension and identified no culturally irrelevant item in the MSAS-Ch.
Phase II The MSAS-Ch (Appendix) was administered to 370 patients with cancer between 2003 and 2005. The proportion of missing responses per item (one of the three dimensions in a symptom) was low, with 2% of patients missing one dimension in a symptom. None of the patients had two dimensions or more missing in a symptom. All of the patients were included in the psychometric analysis after imputing missing values. As shown in Table 1, the mean SD age of the patients was 54.2 11.9 years (range 21e84 years), 193 (52.2%) were females and 56% had secondary or tertiary education. Twenty-four percent were diagnosed with head and neck cancer (n ¼ 88), 22% with breast cancer (n ¼ 80), and 21% with colorectal cancer (n ¼ 76). Most of the patients were
Vol. 37 No. 1 January 2009
Validation of the Chinese Version of the MSAS
Table 1 Demographic and Clinical Characteristics (n¼370) Characteristics
Mean (SD)
Age (years) Duration since diagnosis (months)
54.2 (11.9) 23.5 (30.3)
Gender Male Female
f (%) 177 (47.8) 193 (52.2)
Education level No formal education Primary Secondary Tertiary Unknown
29 126 175 31 9
(7.8) (34.1) (47.3) (8.4) (2.4)
88 80 76 53 28 45
(23.8) (21.6) (20.5) (14.3) (7.6) (12.2)
Stage of cancer I II III IV Unknown
43 78 105 128 16
(11.6) (21.1) (28.4) (34.6) (4.3)
Cancer therapy Chemotherapy Radiotherapy Chemo-radiotherapy
175 (47.3) 78 (21.1) 117 (31.6)
Completion of cancer treatment No Yes
214 (57.8) 156 (42.2)
Treatment setting Outpatient Inpatient
255 (68.9) 115 (31.1)
Site of cancer Head and neck Breast Colorectal Lung Gynecological Others
in Stages III or IV of the disease (63%). More than half were receiving active cancer treatment (n ¼ 247, 58%), and 68.9% (n ¼ 255) were in an outpatient treatment setting. The mean SD and median (range) number of symptoms experienced was 14 3.89 and 13 (8-33). As shown in Table 2, the symptom prevalence was moderate, ranging from 57.3% for dry mouth to 8.9% for problems with urination. The six most prevalent symptoms reported by the patients were dry mouth (57.3%), lack of energy (54.1%), worrying (43.2%), pain (42.4%), change in the way food tastes (39.7%) and difficulty sleeping (39.7%). The highest symptom score was associated with ‘‘pain’’ and ‘‘lack of appetite,’’ with a mean of 2.4. The majority (22 of 32 items) of the symptom scores were >2. Table 2 also summarizes the MSAS-Ch data at the level of
49
each item. All of the items showed a noteworthy degree of variation in frequency, severity and distress. A floor effect (>20% of scores at the lowest possible score) was shown for 12, 32 and 20 of the 32 items in terms of frequency, severity and distress, respectively. By contrast, only 7, 1 and 0 of the 32 items in terms of frequency, severity and distress, respectively, had a ceiling effect (>20% of scores at the highest possible score). A high floor or ceiling effect (>70% of scores at the lowest possible score) was not observed for any of the items in the MSAS-Ch. Referencing the results of the original study,2 a two-factor structure of a hypothesized model of the MSAS-Ch was specified. The overall fit of the data-model with the MSAS-Ch was then examined by the goodness-of-fit indices. The CFA confirmed the two latent factors generated in the original sample of Portenoy et al.:2 PHYS (Factor 1) and PSYCH (factor 2), for the observed data (Fig. 1). In addition, the CFA proved that this two-factor model adequately fit our present sample data. The corresponding goodness-of-fit indices of CFI, GFI, NFI, NNFI and RFI were all above 0.95, and the RMSEA was 0.055, which is less than 0.06 and indicates good fit of the two-factor model of the MSAS-Ch. The internal consistencies of the subscale and total MSAS-Ch were similar to those in the original work.2 The Cronbach’s alpha coefficients of the PHYS (Factor 1) and PSYCH (Factor 2) subscales were 0.79 and 0.81, respectively. The total scale Cronbach’s alpha coefficient for all 32 items (TMSAS-Ch) was high at 0.87. Thirteen of 18 corrected item-total correlations were higher than 0.40, indicating that the items were moderately correlated with the respective subscales. Table 3 also shows that the alpha coefficients, if items were deleted, were comparable to the overall alpha coefficient for each of the two subscales except for Items 1, 10, and 11. To assess the reliability of the MSAS-Ch over time, 61 respondents were selected at random to fill in the MSAS-Ch again within 24e48 hours. The testretest ICC for the PHYS subscale was 0.68, which was at the borderline value for the criterion of test-retest reliability. The test-retest ICC for the PSYCH subscale and TMSAS-Ch were 0.79 and 0.74, separately, all of which were in excess of 0.70.
50
Table 2 MSAS-Ch Item-Level Summary Statistics (n¼370) % Distribution on Response Categories Prevalence Item MSAS-Ch1 MSAS-Ch2 MSAS-Ch3 MSAS-Ch4 MSAS-Ch5 MSAS-Ch6 MSAS-Ch7 MSAS-Ch8 MSAS-Ch9
MSAS-Ch20 MSAS-Ch21 MSAS-Ch22 MSAS-Ch23 MSAS-Ch24 MSAS-Ch25 MSAS-Ch26
Average of Frequency, Severity and Distress Scores
Distress
1
2
3
4
1
2
3
4
0
1
2
3
4
Mean (SD)
134 157 200 131 115 212 106 137 126
(36.2) [40.1] (42.4) [63.1] (54.1) [73.4] (35.4) [29.5] (31.1) [62.4] (57.3) [55.3] (28.6) [44.7] (37) [59.7] (34.1) [36.4]
12.7 8.9 12.5 26.0 22.6 9.9 33.0 14.6 21.4
59.0 42.0 45.0 52.7 50.4 42.5 45.3 42.3 41.3
17.9 23.6 31.5 15.3 18.3 27.4 20.8 30.7 17.5
10.4 25.5 11.0 6.1 8.7 20.3 0.9 12.4 19.8
22.7 25.6 24.6 51.5 43.0 26.7 47.1 31.1 45.2
62.1 40.4 50.8 33.1 38.6 47.1 37.3 43.7 30.2
12.1 23.1 20.6 12.3 13.2 20.0 13.7 22.2 20.6
3.0 10.9 4.0 3.1 5.3 6.2 2.0 3.0 4.0
16.9 16.0 25.6 40.5 21.1 33.7 19.0 42.5 37.1
50.8 24.4 34.2 30.5 36.8 30.8 48.0 21.6 32.3
18.5 28.2 22.6 20.6 22.8 15.9 23.0 23.1 15.3
10.8 19.2 13.6 6.1 12.3 14.9 8.0 9.7 12.1
3.1 12.2 4.0 2.3 7.0 4.8 2.0 3.0 3.2
2.0 2.4 2.1 1.8 2.0 2.2 1.8 2.0 2.0
(0.6) (0.8) (0.7) (0.7) (0.8) (0.8) (0.7) (0.7) (0.8)
147 68 33 50 110 57 139 97 160 38
(39.7) [52.8] (18.4) [38.7] (8.9) [15.6] (13.5) [21.1] (29.7) [22.9] (15.4) [23.9] (37.6) [67.4] (26.2) [a] (43.2) [72.4] (10.3) [23.3]
12.2 13.4 18.2 38.0 27.3 28.1 22.3 14.4 14.4 21.1
46.9 44.8 39.4 52.0 46.4 52.6 42.4 44.3 53.1 26.3
24.5 19.4 9.1 10.0 17.3 15.8 25.2 34.0 20.6 7.9
16.3 22.4 33.3 0.0 9.1 3.5 10.1 7.2 11.9 44.7
25.5 32.4 54.8 30.0 39.1 38.6 32.8 28.9 30.2 23.7
44.1 39.7 35.5 52.0 46.4 52.6 40.1 37.1 44.7 34.2
20.0 23.5 3.2 16.0 11.8 7.0 21.2 28.9 17.0 21.1
10.3 4.4 6.5 2.0 2.7 1.8 5.8 5.2 8.2 21.1
22.8 22.1 33.3 18.4 21.8 29.8 11.7 41.2 12.6 47.4
31.7 35.3 46.7 38.8 45.5 43.9 32.8 28.9 34.6 28.9
21.4 17.6 6.7 26.5 18.2 15.8 29.9 9.3 28.9 15.8
13.1 20.6 10.0 14.3 10.0 7.0 18.2 18.6 14.5 7.9
11.0 4.4 3.3 2.0 4.5 3.5 7.3 2.1 9.4 0.0
2.2 2.2 2.0 1.9 1.9 1.8 2.2 2.0 2.2 2.2
(0.8) (0.8) (0.8) (0.6) (0.7) (0.6) (1.0) (0.7) (0.8) (0.8)
91 132 103 79 129 89 147
(24.6) (35.7) (27.8) (21.4) (34.9) (24.1) (39.7)
20.9 13.6 31.1 20.3 15.5 d d
50.5 37.1 58.3 32.9 55.0 d d
17.6 24.2 9.7 22.8 12.4 d d
11.0 25.0 1.0 24.1 17.1 d d
41.8 21.7 53.4 22.8 33.6 32.6 30.6
39.6 42.6 35.9 39.2 39.8 38.2 37.4
13.2 21.7 10.7 21.5 18.0 16.9 19.7
5.5 14.0 0.0 16.5 8.6 12.4 12.2
31.9 15.5 29.4 16.5 17.2 17.0 17.9
36.3 31.0 44.1 31.6 38.3 34.1 39.3
16.5 20.2 19.6 22.8 21.9 23.9 24.8
12.1 20.2 5.9 17.7 13.3 17.0 9.0
3.3 13.2 1.0 11.4 9.4 8.0 9.0
1.9 2.4 1.7 2.3 2.1 2.1 2.1
(0.8) (0.9) (0.6) (0.9) (0.8) (0.9) (0.9)
137 139 81 43 84 121
(37) [27] (37.6) [17.1] (21.9) [33.6] (11.6) [27.5] (22.7) [28.2] (32.3) [a]
d d d d d d
d d d d d d
d d d d d d
d d d d d d
42.3 32.4 29.6 44.2 41.7 37.2
32.8 23.7 39.5 34.9 32.1 43.8
18.2 27.3 22.2 16.3 15.5 15.7
6.6 16.5 8.6 4.7 10.7 3.3
30.1 34.1 21.5 23.8 14.5 35.8
30.9 26.7 36.7 38.1 34.9 29.2
22.1 20.7 22.8 23.8 24.1 24.2
10.3 11.1 11.4 9.5 14.5 7.5
6.6 7.4 7.6 4.8 12.0 3.3
1.9 2.1 2.0 1.8 2.1 1.8
(0.8) (0.9) (0.8) (0.8) (0.9) (0.7)
[27.8] [44.5] [23.5] [10.9] [47.2] [12.9] [37.2]
Numbers in [ ] represent prevalence (%) of the item in the original MSAS. a Not reported in the original MSAS.
Vol. 37 No. 1 January 2009
MSAS-Ch27 MSAS-Ch28 MSAS-Ch29 MSAS-Ch30 MSAS-Ch31 MSAS-Ch32
Severity
Cheng et al.
MSAS-Ch10 MSAS-Ch11 MSAS-Ch12 MSAS-Ch13 MSAS-Ch14 MSAS-Ch15 MSAS-Ch16 MSAS-Ch17 MSAS-Ch18 MSAS-Ch19
Difficulty concentrating Pain Lack of energy Cough Feeling nervous Dry mouth Nausea Feeling drowsy Numbness/tingling in hands/feet Difficulty sleeping Feeling bloated Problems with urination Vomiting Shortness of breath Diarrhea Feeling sad Sweats Worrying Problems with sexual interest or activity Itching Lack of appetite Dizziness Difficulty swallowing Feeling irritable Mouth sores Change in the way food tastes Weight loss Hair loss Constipation Swelling of arms or legs ‘‘I don’t look like myself’’ Changes in skin
f (%)
Frequency
Vol. 37 No. 1 January 2009
0.58
MSAS-Ch2
0.70
MSAS-Ch3
0.65
0.68
MSAS-Ch6
0.54
0.79
MSAS-Ch7
0.56
0.80
MSAS-Ch8
0.95
MSAS-Ch11
0.69
MSAS-Ch13
0.58
MSAS-Ch21
0.93
MSAS-Ch22
0.47
0.78
MSAS-Ch26
0.55
0.70
MSAS-Ch27
0.45
0.79
MSAS-Ch29
0.73
MSAS-Ch1
0.59
MSAS-Ch5
0.72
MSAS-Ch10
0.53
0.41
MSAS-Ch16
0.77
0.44
MSAS-Ch18
0.69
MSAS-Ch24
Validation of the Chinese Version of the MSAS
0.46 0.45 0.21 0.33
PHYS
0.65 0.27
0.69
0.52 0.64
PSYCH
0.75 0.71
Fig. 1. Confirmatory factor analysis (n ¼ 370).
The mean SD of the PHYS, PSYCH, GDI, and TMSAS-Ch scores were 1.93 0.57, 2.01 0.64, 1.87 0.73, and 1.91 0.52, respectively. Pearson cross-correlations for the MSAS-Ch and the validation measures are shown in Table 4. As expected, the MSAS-Ch PHYS subscale showed the highest correlation with the FACT-G (Ch) Phy subscale (r ¼ 0.62, P < 0.01) relative to other FACT-G (Ch) subscale scores. The PSYCH subscale also correlated moderately with the FACT-G (Ch) Emt subscale (r ¼ 0.51, P < 0.01), Phy subscale (r ¼ 0.58, P < 0.01), and total score (r ¼ 0.53, P<0.01). In addition, the TMSASCh correlated moderately with the FACT-G (Ch) total score (r ¼ 0.58, P < 0.01), but its highest correlation was with the FACT-G (Ch) Phy subscale (r ¼ 0.65, P < 0.01), supporting the concurrent validity. The results were consistent with those reported in the original version.2 The Pearson correlation coefficients between scores on the MSAS-Ch PHYS and SDS total were 0.53 (P < 0.01) and between
51
the MSAS-Ch PSYCH and SDS total were 0.49 (P < 0.01), revealing a satisfactory convergent validity. The MSAS-Ch PHYS subscale (r ¼ 0.49, P < 0.01) and total (r ¼ 0.46, P < 0.01) scores correlated moderately with the severity score on the BPI-Ch. To replicate the original MSAS,2 Pearson cross-correlations for the MSAS-Ch GDI and the validation measures were computed. As shown in Table 4, the MSAS-Ch GDI was moderately correlated with the FACT-G (Ch) (r ¼ 0.55e0.64, P < 0.01) except for the Soc/Fam and Doc subscales, and with the SDS total score (r ¼ 0.56, P < 0.01). This result indicates that the MSAS-Ch did measure the symptom distress dimension in the same ways as the original MSAS.2 The mean SD KPS score was 90.35 11.24 (median 90; range 50e100); 89.7% of the subjects had a KPS score $80. Owing to the small number of subjects who were rated ‘‘20e50’’ on KPS (n ¼ 4, 1.1%), these were pooled into the ‘‘60e70’’ category. As shown in Table 5, patients with high performance status (KPS) had a lower score on the MSAS-Ch subscales and total than the subjects with poor performance status. The Kruskal-Wallis test showed that the MSAS-Ch subscales and total scores were significantly different using the KPS (P < 0.01), supporting the known group validity. Post hoc analysis using Mann-Whitney U tests with Bonferroni correction showed that subjects with a KPS score of 90e100 reported significantly lower scores on the MSAS-Ch subscales and total than those subjects with KPS scores of 80 (P < 0.01) and 50e70 (P < 0.01). As for the treatment setting, inpatients had a higher score on the MSAS-Ch subscales and total than the outpatients (P < 0.01), revealing a known group validity in which MSAS-Ch was able to differentiate between subjects according to their hospitalization status (Table 6). These results were consistent with those reported in the original version.2
Discussion Although MSAS has been developed in English-speaking cultures and applied in various settings, such as in patients with advanced and terminal cancer in the palliative care setting, noncancer hospitalized patients near the end of life, and the pediatric oncology setting, it is not clear whether the MSAS can also
52
Cheng et al.
Vol. 37 No. 1 January 2009
Table 3 Reliability Analysis of the MSAS-Ch (n ¼ 370) Internal Consistency Item
Squared Multiple Correlation
Corrected Item-Total Correlation
PHYS MSAS-Ch26 Change in the way food tastes MSAS-Ch27 Weight loss MSAS-Ch7 Nausea MSAS-Ch13 Vomiting MSAS-Ch8 Feeling drowsy MSAS-Ch21 Lack of appetite MSAS-Ch3 Lack of energy MSAS-Ch2 Pain MSAS-Ch6 Dry mouth MSAS-Ch11 Feeling bloated MSAS-Ch22 Dizziness MSAS-Ch29 Constipation
0.365
0.491
0.430 0.386 0.347 0.318 0.426 0.339 0.339 0.265 0.076 0.098 0.213
0.544 0.437 0.385 0.437 0.611 0.488 0.488 0.476 0.180 0.255 0.388
PSYCH MSAS-Ch24 MSAS-Ch5 MSAS-Ch16 MSAS-Ch18 MSAS-Ch10 MSAS-Ch1
0.414 0.413 0.513 0.528 0.187 0.156
0.623 0.615 0.694 0.693 0.431 0.387
Feeling irritable Feeling nervous Feeling sad Worrying Difficulty sleeping Difficulty concentrating
be applied in other cultural contexts and oncology settings. This study revealed a high prevalence of certain symptoms in patients during cancer therapy or during the early posttreatment period, particularly dry mouth, lack of energy, worrying, and pain. This finding is similar to the findings of previous studies with various cancer populations and oncology settings.1e3,9,37 Similar to Tranmer et al., the highest symptom score was associated with pain, and for the majority of symptoms, if Table 4 Correlations for the MSAS-Ch and the Validation Measures (n¼370) MSAS-Ch
FACT-G(Ch) Phy Soc/Fam Doc Emt Fnt Total SDS-Ch Total BPI-Ch Pain severity (n¼157) Pain interference (n¼157) a
P<0.01.
PHYS
PSYCH
GDI
TMSAS
0.620a 0.153a 0.104 0.409a 0.443a 0.527a
0.578a 0.111 0.007 0.510a 0.432a 0.525a
0.643a 0.222a 0.153a 0.564a 0.547a 0.637a
0.650a 0.198a 0.106 0.507a 0.483a 0.580a
0.529a
0.485a
0.560a
0.582a
0.487a
0.294a
0.346a
0.459a
0.304a
0.232
0.392a
0.320a
Cronbach Alpha if Alpha Item Deleted 0.795
0.775 0.771 0.781 0.787 0.781 0.761 0.776 0.776 0.777 0.802 0.795 0.785
0.810
0.769 0.773 0.750 0.751 0.813 0.817
present, the patients reported that they were frequent, moderately severe to severe, and somewhat to considerably distressing.9 The present study demonstrates that the Chinese version of the MSAS has adequate psychometric properties of internal consistency, concurrent and convergent validity, and known group validity when applied to Hong Kong Chinese patients with cancer, and that these values are equivalent to those of the original scale. CFA confirmed the construct validity of the MSAS-Ch, with a good fit between the hypothesized model and the observed data. Translation and adaptation of English-speaking cultures’ instruments has been a trend around the world. Linguistic and conceptual equivalences, as well as content relevance taking cultural characteristics into account, are fundamental requirements of all translated instruments and are prerequisite for establishing other types of validity. In this study, a rigorous multistepped translation process was undertaken in developing the MSAS-Ch. The expert content review revealed a high degree of agreement among seven experts in terms of content relevancy and semantic equivalence, indicating that the meanings of the items were the same after translation into Chinese and the items were relevant within the Hong Kong Chinese culture, thereby revealing the
Vol. 37 No. 1 January 2009
Validation of the Chinese Version of the MSAS
53
Table 5 Difference in MSAS-Ch Subscale Scores According to KPS KPS 50e70 (n¼38)
KPS 80 (n¼58)
KPS 90e100 (n¼274)
MSAS-Ch Mean
SD
Median
Range
Mean
SD
Median
Range
Mean
SD
Median
Range
c2
P-value
PHYS PSYCH GDI TMSAS
0.53 0.69 0.59 0.53
2.40 2.30 2.10 2.29
0.93e3.48 1.53e3.94 1.27e3.90 1.31e3.50
2.23 2.23 2.22 2.16
0.59 0.64 0.64 0.51
2.09 2.16 2.13 2.07
1.38e3.64 1.27e4.00 0.80e3.60 1.48e3.66
1.79 1.88 1.68 1.78
0.51 0.59 0.73 0.46
1.76 1.77 1.60 2.07
0.90e3.58 0.93e4.00 0.80e3.53 0.93e3.20
51.3 25.3 62.6 52.0
<0.01 <0.01 <0.01 <0.01
2.38 2.42 2.55 2.36
content validity of the MSAS-Ch. In addition, the factor structure of original MSAS was found to have cross-cultural comparability, in which the two latent factors being identified in American populations could adequately fit our present sample data. Using the variety of fit statistics in CFA, the results were convincing and confirmed the construct validity of the MSAS-Ch, with a good fit between the hypothesized model and our present sample data. Nevertheless, it is noteworthy that two items, MSAS-Ch 11 (Feeling bloated) and 22 (Dizziness) were weakly loaded onto the PHYS subscale, unlike the loading reported by Portenoy et al.2 A similar weak factor loading of the ‘‘Feeling bloated’’ item on PHYS was also reported by Tranmer and colleagues.9 One probable explanation is less prevalence of feeling bloated in our study sample compared with the sample for the original study. Another possible reason could be the low symptom score yielded in dizziness in our present sample. Nevertheless, there is uncertainty about whether the prevalence of symptoms or the constellation of shared dimensions of symptoms encompassing frequency, intensity, and level of perceived distress would affect the strength of the factor loading. Further work using Rasch analysis is needed to complement the traditional psychometric evaluation of validity to permit a detailed examination of the structure of MSAS-Ch. The MSAS-Ch PHYS and PSYCH subscales had a high degree of internal consistency, thus supporting their reliability. In addition,
the testeretest reliabilities were good, meeting the ICC coefficient requirements. The relative low value of ICC for the PHYS subscale was possibly due to change of some physical symptoms within 24e48 hours. In comparison with the original validation (0.84e0.88),2 the MSAS-Ch subscale alpha coefficients were relatively low. In particular, the alpha coefficient for the MSAS-Ch PHYS (0.79) was lower than that for the coefficient obtained in the original study (0.88). Notably, the coefficients of corrected item-total correlation of ‘‘Feeling bloated’’ (0.18) and ‘‘Dizziness’’ (0.26) were very low, which appeared to affect the interitem correlation structure and thus the internal consistency of the MSAS-Ch PHYS subscale. In addition, differences in the symptom prevalence between the present and the original study may contribute to the relatively low alpha coefficient of MSAS-Ch PHYS. In the study of Portenoy et al., the PHYS subscale contained 12 symptoms that were relatively prevalent in their sample.2 Those patients reported a higher prevalence of pain, lack of energy, nausea and vomiting, feeling drowsy and bloated, lack of appetite and constipation, as compared with our study sample. There is some uncertainty about whether the prevalence of symptoms would affect the interitem correlation structure and thus the internal consistency of the subscale. The subscale scores of MSAS-Ch were moderately correlated with the scores on various validation measures that assessed psychological distress, pain, and HRQoL, reflecting that
Table 6 Differences in MSAS-Ch Subscale Scores According to the Hospital Setting Inpatient Setting (n¼119)
PHYS PSYCH GDI TMSAS
Outpatient Setting (n¼251)
Mean
SD
Median
Range
Mean
SD
Median
Range
z
P-value
2.07 2.10 2.09 2.05
0.58 0.63 0.71 0.52
1.98 1.87 2.00 1.99
0.90e3.58 0.93e4.00 0.80e3.90 0.93e3.20
1.86 1.95 1.75 1.84
0.55 0.65 0.71 0.50
1.77 1.84 1.68 1.77
0.90e3.64 1.13e3.94 0.80e3.53 1.22e3.66
3.3 2.096 3.956 3.208
<0.01 0.036 <0.01 <0.01
54
Cheng et al.
they were measuring a similar construct. These findings were coherent and comparable to those in the original study.2 Construct validity was also supported by comparing the MSASCh scores in subpopulations that varied clinically. Inpatients and patients with poorer performance status scored higher on the MSAS subscale and total scores than outpatients and patients with higher performance status. The floor effect for the sample was modest, whereas the ceiling effect was negligible. One possible explanation for the floor effect is that the majority of the patients in this sample had only mildly distressing symptoms and so would be expected to score at the floor level. Nevertheless, further evaluation of the floor and ceiling effects is warranted, to include a larger population with severely distressing symptoms. In addition, further research should be conducted using item response analysis to investigate the performance of individual item response choices.
Conclusion The MSAS-Ch has demonstrated an acceptable level of psychometric properties, and cross-cultural equivalence with the original version, as a multidimensional instrument that measures the prevalence and burden of common symptoms in cancer patients. The MSAS-Ch would provide a common platform for use by clinicians to assess physical and psychological symptoms during cancer therapy and at the early post-treatment stage in Chinese-speaking patients. In addition, the MSAS-Ch could facilitate the exploration of the effectiveness of symptom management strategies. Nevertheless, instrument validation is by no means a one-time event. In future studies, an attempt should be made to determine if certain items in MSAS-Ch behave differently across cancer diagnostic groups and the disease trajectory based on modern psychometric evaluation of differential item functioning. Rasch analysis is also highly recommended so as to enable a detailed examination of the structure of the MSAS-Ch.
Vol. 37 No. 1 January 2009
the early stages of the study. Likewise, they are also grateful to the oncologists and nurses for their assistance with the content validation. Finally, they wish to express their gratitude to the patients for agreeing to participate in this study.
References 1. Naughton M, Homsi J. Symptom assessment in cancer patients. Curr Oncol Rep 2002;4:256e263. 2. Portenoy RK, Thaler HT, Kornblith AB, et al. The Memorial Symptom Assessment Scale: an instrument for the evaluation of symptom prevalence, characteristics and distress. Eur J Cancer 1994; 30A(9):1326e1336. 3. Chang VT, Hwang SS, Feuerman M, et al. Symptom and quality of life survey of medical oncology patients at a Veterans Affairs Medical Center: a role for symptom assessment. Cancer 2000;88: 1175e1183. 4. Donnelly S, Walsh D, Rybicki L. The symptoms of advanced cancer: identification of clinical and research priorities by assessment of prevalence and severity. J Palliat Care 1995;11(1):27e32. 5. Ruthledge DM, McGuire C. Evidence-based symptom management. In: Yarbro CH, Frogge MH, Goodman M, eds. Cancer symptom management, 3rd ed. Sudbury, MA: Jones & Bartlett, 2003. 6. Paice JA. Assessment of symptom clusters in people with cancer. J Nat Cancer Instit Mono 2004;32:98e102. 7. Chang VT, Hwang SS, Feuerman M. Validation of the Edmonton Symptom Assessment Scale. Cancer 2000;88:2164e2171. 8. Portenoy RK, Thaler HT, Kornblith AB, et al. Symptom prevalence, characteristics and distress in a cancer population. Qual Life Res 1994;3: 183e189. 9. Tranmer JE, Heyland D, Dudgeon D, et al. Measuring the symptom experience of seriously ill cancer and noncancer hospitalized patients near the end of life with the Memorial Symptom Assessment Scale. J Pain Symptom Manage 2003;25:420e429. 10. Armstrong TS. Symptoms experience: a concept analysis. Oncol Nurs Forum 2003;30(4):601e606. 11. Alexander J. Symptom management. In: Gukkatte MM, ed. Clinical guide to antineoplastic therapy: a chemotherapy handbook. Pittsburgh, PA: Oncology Nursing Society, 2001: 297e313.
Acknowledgments
12. Brescia FJ, Adler D, Gray G, et al. Hospitalized advanced cancer patients: a profile. J Pain Symptom Manage 1990;5:221e227.
The authors’ special thanks go to Mr. Bernard Yam for his exemplary assistance during
13. Coyle N, Adelhardt J, Foley KM, Portenoy RK. Character of terminal illness in the advanced cancer
Vol. 37 No. 1 January 2009
Validation of the Chinese Version of the MSAS
patient: pain and other symptoms during the last four weeks of life. J Pain Symptom Manage 1990;5: 85e93. 14. Kutner JS, Bryant LL, Beaty BL, et al. Time course and characteristics of symptom distress and quality of life at the end of life. J Pain Symptom Manage 2007;5:85e93. 15. Vainio A, Auvinen A. Prevalence of symptoms among patients with advanced cancer: an international collaborative study. J Pain Symptom Manage 1996;12(1):3e10. 16. Collins JJ, Devine TD, Dick GS, et al. The measurement of symptoms in young children with cancer: the validation of the Memorial Symptom Assessment Scale in children aged 7e12. J Pain Symptom Manage 2002;23(1):10e16. 17. Yu CLM, Fielding R, Chan CLW, et al. Measuring quality of life of Chinese cancer patients: a validation of the Chinese version of the functional assessment of cancer therapy-general (FACT-G) scale. Cancer 2000;88:1715e1727. 18. Chan WH. The Chinese translation of the revised Piper Fatigue Scale and Symptom Distress Scale. Research Report, . The Chinese University of Hong Kong. Hong Kong: The Nethersole School of Nursing, 2000. 19. Wang XS, Mendoza TR, Gai S, Cleeland CS. The Chinese version of the Brief Pain Inventory (BPI-C): its development and use in a study of cancer pain. Pain 1996;67:407e416. 20. Yates JW, Chalmer B, McKegney FP. Evaluation of patients with advanced cancer using the Karnofsky Performance Status. Cancer 1980;40: 2222e2224.
55
size in confirmatory factor analysis models. In: Gaul W, Schader M, eds. Classification as a tool of research. Amsterdam: Elsevier Science Publishers, 1986: 3e10. 26. Steiger JH. Changing causal relationships without changing the fit: some rules for generating equivalent LISREL models. Multivariate Behav Res 1990;21:309e331. 27. Tucker L, Lewis C. A reliability coefficient for maximum likelihood factor analysis. Psychometrika 1973;38:1e10. 28. Bentler PM, Bonett DG. Significance tests and goodness of fit in the analysis of covariance structures. Psychol Bull 1980;88:588e606. 29. MacCallum RC, Hong S. Power analysis in covariance structure modeling using GFI and AGFI. Multivariate Behav Res 1997;32:193e210. 30. Marsh HW, Balla JR, Hau KT. An evaluation of incremental fit indices: a clarification of mathematical and empirical properties. In: Marcoulides GA, Schumacker RE, eds. Advanced structural equation modelling: Issues and techniques. Hillsdale, NJ: Erlbaum Publishers, 1996: 315e535. 31. Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equation Model 1999;6(1):1e55. 32. Pett MA, Lackey NR, Sullivan JJ. Making sense of factor analysis: The use of factor analysis in health care research. Thousand Oaks, CA: Sage Publications, 2003. 33. Nunnally JC, Bernstein IH. Psychometric theory, 3rd ed. New York: McGraw-Hill, 1994.
21. Davis LL. Instrument review: getting the most from a panel of experts. Applied Nurs Res 1992;5: 194e197.
34. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986;1:307e310.
22. Haynes SN, Richard DCS, Kubany ES. Content validity in psychological assessment: a functional approach to concepts and methods. Psychol Assessment 1995;7:238e247.
35. Sitzia J, Dikken C, Hughes J. Psychometric evaluation of a questionnaire to document side-effects of chemotherapy. J Advanced Nurs 1997;25: 999e1007.
23. Lynn MR. Determination and quantification of content validity. Nurs Res 1986;35(6):382e385.
36. Denegar CR, Ball DW. Assessing reliability and precision of measurement: an introduction to intraclass correlation and standard error of measurement. J Sport Rehab 1993;2:35e42.
24. Alexander M, Berger W, Buchholz P, et al. The reliability, validity, and preliminary responsiveness of the eye allergy patient impact questionnaire. Health Qual Life Outcomes 2005;3(1):67. 25. Gaul W, Schader M. The robustness of LISREL unweighted squares estimation against small sample
37. Homsi J, Walsh D, Rivera N, et al. Symptom evaluation in palliative medicine: patients report vs systematic assessment. Support Care Cancer 2006; 14(5):444e453.
56
Cheng et al.
Vol. 37 No. 1 January 2009
Appendix MSAS-Ch
“X
? 1.
1
2
3
4
1
2
3
4
0
1
2
3
4
2.
1
2
3
4
1
2
3
4
0
1
2
3
4
3.
1
2
3
4
1
2
3
4
0
1
2
3
4
4.
1
2
3
4
1
2
3
4
0
1
2
3
4
5.
1
2
3
4
1
2
3
4
0
1
2
3
4
6.
1
2
3
4
1
2
3
4
0
1
2
3
4
7.
1
2
3
4
1
2
3
4
0
1
2
3
4
8.
1
2
3
4
1
2
3
4
0
1
2
3
4
1
2
3
4
1
2
3
4
0
1
2
3
4
10.
1
2
3
4
1
2
3
4
0
1
2
3
4
11.
1
2
3
4
1
2
3
4
0
1
2
3
4
12.
1
2
3
4
1
2
3
4
0
1
2
3
4
13.
1
2
3
4
1
2
3
4
0
1
2
3
4
14.
1
2
3
4
1
2
3
4
0
1
2
3
4
15.
1
2
3
4
1
2
3
4
0
1
2
3
4
16.
1
2
3
4
1
2
3
4
0
1
2
3
4
17.
1
2
3
4
1
2
3
4
0
1
2
3
4
18.
1
2
3
4
1
2
3
4
0
1
2
3
4
19.
1
2
3
4
1
2
3
4
0
1
2
3
4
20.
1
2
3
4
1
2
3
4
0
1
2
3
4
21.
1
2
3
4
1
2
3
4
0
1
2
3
4
22.
1
2
3
4
1
2
3
4
0
1
2
3
4
23.
1
2
3
4
1
2
3
4
0
1
2
3
4
24.
1
2
3
4
1
2
3
4
0
1
2
3
4
9.
/
Vol. 37 No. 1 January 2009
Validation of the Chinese Version of the MSAS
"
"
57
"X"
25.
1
2
3
4
0
1
2
3
4
26.
1
2
3
4
0
1
2
3
4
27.
1
2
3
4
0
1
2
3
4
28.
1
2
3
4
0
1
2
3
4
29.
1
2
3
4
0
1
2
3
4
30
1
2
3
4
0
1
2
3
4
1
2
3
4
0
1
2
3
4
1
2
3
4
0
1
2
3
4
31." 32.
" (
)
Journal of Pain and Symptom Management
Vol. 37 No. 1 January 2009
Original Article
Measuring the Symptom Experience of Chinese Cancer Patients: A Validation of the Chinese Version of the Memorial Symptom Assessment Scale Karis K.F. Cheng, RN, PhD, PDip Epidemiol&Biostat, Eric M.C. Wong, MA, W.M. Ling, RN, MN, Carmen W.H. Chan, RN, PhD, and David R. Thompson, RN, PhD The Nethersole School of Nursing (K.K.F.C., C.W.H.C.), and Division of Biostatistics, School of Public Health (E.M.C.W.), The Chinese University of Hong Kong, Hong Kong; Department of Clinical Oncology (W.M.L.), Pamela Youde Nethersole Eastern Hospital, Hong Kong; and Departments of Health Sciences and Cardiovascular Sciences (D.R.T.), University of Leicester, Leicester, United Kingdom
Abstract The purpose of this study was to translate the Memorial Symptom Assessment Scale (MSAS) into Chinese and evaluate the psychometric properties of this version. The original MSAS is a 32-item, patient-rated measure that was developed to assess common cancer-related physical and psychological symptoms with respect to frequency, intensity, and distress. In this study, a two-phase design was used. Phase I involved iterative forwardebackward translation, testing of content validity (CVI) and a pretest. Phase II established the psychometric properties of the Chinese version MSAS (MSAS-Ch). Results showed that the MSAS-Ch achieved content relevancy CVI of 0.94 and semantic equivalence CVI of 0.94. Pretesting was performed in 10 cancer patients, and the results revealed adequate content coverage and comprehensibility of the MSAS-Ch. A convenience sample of 370 patients undergoing cancer therapy or at the early post-treatment stage was recruited for psychometric evaluation. Confirmatory factor analysis confirmed the construct validity of the MSAS-Ch, with a good fit between the factor structure of the original version and our present sample data (goodnessof-fit indices all above 0.95). The internal consistency reliability of subscales and total MSAS-Ch was moderately high, with Cronbach alpha coefficients ranging from 0.79 to 0.87. The testeretest intraclass correlation results for the subscale and total MSAS-Ch ranged from 0.68 to 0.79. The subscale scores of MSAS-Ch were moderately correlated with the scores on various validation measurements that assessed psychological distress, pain, and healthrelated quality of life (r ¼ 0.46e0.65, P < 0.01), confirming that they were measurements of similar constructs. The validity of the construct validity was also supported by comparing the MSAS-Ch scores for subpopulations that varied clinically. Inpatients and patients with poorer performance status scored higher on the MSAS-Ch subscale and total scores than outpatients and patients with higher performance status (P < 0.05). Our study shows that
This study was partially supported by the Lee Hysan Foundation Fund, United College of the Chinese University of Hong Kong. Address correspondence to: Karis Kin-Fong Cheng, RN, PhD, Room 732, Esther Lee Building, Chung Chi Ó 2009 U.S. Cancer Pain Relief Committee Published by Elsevier Inc. All rights reserved.
College, The Nethersole School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong. E-mail: [email protected] Accepted for publication: December 28, 2007. 0885-3924/09/$esee front matter doi:10.1016/j.jpainsymman.2007.12.019
Vol. 37 No. 1 January 2009
Validation of the Chinese Version of the MSAS
45
the MSAS-Ch has adequate psychometric properties of validity and reliability, and can be used to assess symptoms during cancer therapy and at the early post-treatment stage in Chinese-speaking patients. J Pain Symptom Manage 2009;37:44e57. Ó 2009 U.S. Cancer Pain Relief Committee. Published by Elsevier Inc. All rights reserved. Key Words Symptoms, cancer, MSAS, validation
Introduction Patients with cancer suffer from a multitude of intense physical and psychological symptoms regardless of the stage of the disease.1,2 Some symptoms are related to progression of the disease, whereas others are associated with the early and late effects of cancer treatment, or to problems with psychosocial adjustment.3,4 Symptoms seldom occur in isolation in patients with cancer.5 Previous studies have demonstrated that symptoms, such as pain, depression, fatigue, and others, occur in clusters and can influence the occurrence of other symptoms. The concept of symptom clusters is becoming increasingly recognized as an important platform for symptom management for cancer patients.5,6 In fact, many surveys have documented a high prevalence of the grouping of symptoms in cancer patients, with a median number ranging from eight to 11 symptoms per patient.3,4,7e9 Efforts to describe symptoms have shown that each symptom has an associated constellation of shared dimensions, including frequency, intensity, and level of perceived distress.10 Health care professionals experienced in oncology care know that the morbidity of cancer-related symptoms can be profound and very distressing to patients. In addition to compromising the functional capacity of the patient, cancer-related symptoms can significantly increase psychological distress and alter the patient’s health-related quality of life (HRQoL).3,8 The resulting morbidity and its impact on HRQoL can even affect the patient’s decision to continue with treatment, with an obvious impact on the prospects of surviving the disease.11 During the past decade, there has been a substantial increase in the concern and attention given to symptom control and HRQoL of patients with a variety of malignancies and
disease trajectories. The literature has repeatedly highlighted that comprehensive symptom assessment is a prerequisite for good symptom control and HRQoL improvement.2,3,8 Assessment and measurement of cancer-related symptoms is important at all stages of the illness.9 At present, few studies have been undertaken that systematically assess the prevalence and burden of cancer-related symptoms. Most of the published studies refer to the symptom experience in patients at the end of life,9,12e15 while ignoring their experience during diagnosis, treatment, and disease remission. Little is known about the prevalence, severity and distress caused by symptoms in the Chinese cancer population. One of the barriers to conducting such studies has been the lack of validated comprehensive symptom assessment tools. The Memorial Symptom Assessment Scale (MSAS) is one of the few available comprehensive cancer-related symptom assessment tools.2,3 The format of the MSAS instrument allows for more detailed analyses of symptom severity and distress, in addition to estimates of symptom prevalence. The original development and validation of the MSAS involved item generation and scale construction, initial evaluation and re-evaluation of factors, reliability and validity, and the differentiation of various clinical populations. The overall validation results were good. Initial and subsequent factor analysis of the MSAS items resulted in a two-factor solution that supports the meaningful groupings of symptoms.2,16 The reported correlations with the Functional Living Index-Cancer were 0.60 to 0.78, and with the Karnofsky Performance Status Scale (KPS) were 0.31 to 0.58.2 The correlation with the FACT-G Sum Quality of Life was 0.65, with subscale correlation coefficients ranging from 0.64 to 0.76.3 Cronbach alpha for the MSAS subscales ranged from 0.82 to 0.88.2,3 Test-retest reliability for the whole scale
46
Cheng et al.
within 24e48 hours was 0.70.16 The aim of this study was to translate the MSAS into Chinese and determine whether the factor structure, and the reliability and validity of the translated MSAS, are suitable for use with Chinese cancer patients.
Methods Sample The study was conducted in a regional hospital in Hong Kong, with the approval of the Institutional Review Board. The subjects recruited were over 18 years of age, had been diagnosed with solid tumors, and were able to understand the study and give informed consent. They were being treated with chemotherapy or radiotherapy, or were in the first 12 months of the post-treatment stage. Patients with a medical diagnosis of encephalopathy or a psychiatric disease that could make evaluation difficult were excluded from the study. Two groups of subjects were recruited for this study. The subjects in Sample 1 were involved in the pretesting. The subjects in Sample 2 were involved in the testing of the main psychometric properties of the Chinese version MSAS (MSAS-Ch). The study was conducted in accordance with the pertinent sections of the Declaration of Helsinki. All participants gave their written informed consent before being enrolled in the study. The enrolled subjects were given a packet of questionnaires, which included the MSAS-Ch, the Chinese version of the Functional Assessment of Cancer Therapy-General (FACT-G [Ch]),17 the Chinese version of the Symptom Distress Scale (SDS-C)18 and, if they had pain, the Chinese version of the Brief Pain Inventory Short Form (BPI-C).19 All of the instruments were completed on the same day in a random order. Observer ratings of KPS were also gathered.20
Instruments MSAS. The MSAS is a multidimensional instrument that evaluates 32 common physical and psychological symptoms. Twenty-four symptoms are evaluated with respect to frequency, intensity, and distress, and eight symptoms are evaluated in terms of severity and distress.2 In the MSAS, each symptom is recorded as present or absent, and if present, is
Vol. 37 No. 1 January 2009
rated using a four- or five-point rating scale for frequency, severity, and associated distress during the previous seven days, with higher scores indicating greater frequency, more severity, and higher distress. If a symptom is absent, each dimension is scored as 0 and the score for that symptom is 0. If a symptom is present, the symptom score is an average of its dimensions. The scoring of the MSAS yields several subscale scores, including a Physical Symptom subscale score (PHYS), a Psychological Symptom subscale score (PSYCH), and a Global Distress Index (GDI). The PHYS is the average of the score for the 12 symptoms: lack of appetite, lack of energy, pain, feeling drowsy, constipation, dry mouth, nausea, vomiting, change in food taste, weight loss, feeling bloated, and dizziness. The PSYCH is the average of the score for the six symptoms: worrying, feeling sad, feeling nervous, difficulty sleeping, feeling irritable, and difficulty concentrating. The GDI is the average of the frequency of four psychological symptoms (feeling sad, worrying, feeling irritable, feeling nervous) and the distress associated with six physical symptoms (lack of appetite, lack of energy, pain, feeling drowsy, constipation, dry mouth). The Total MSAS score (TMSAS) is the average of the symptom scores of all 32 symptoms in the MSAS.2 FACT-G (Ch). The FACT-G (Ch) is a 29-item general measure of HRQoL for Chinese cancer patients. Each item is scored from 0 to 4, anchored from ‘‘not at all’’ to ‘‘very much.’’ The FACT-G (Ch) has five subscales: physical well-being (Phy) (7 items), social/family wellbeing (Soc/Fam) (7 items), relationship with physician (Doc) (2 items), emotional wellbeing (Emt) (5 items), and functional well-being (Fnt) (7 items), with the total FACT-G (Ch) score ranging from 0 to 116. A lower score indicates a poorer level of HRQoL.17 SDS-C. The SDS-C provides a valid measure of global symptom distress in Chinese cancer patients. The SDS-C is a 13-item scale evaluating 11 symptoms. Symptoms are scored from 1 ‘‘no distress’’ to 5 ‘‘extreme distress.’’ The SDS-C total score is the sum of the scores for all 13 items.18 BPI-C. The BPI-C is an 11-item instrument for Chinese cancer patients to assess the
Vol. 37 No. 1 January 2009
Validation of the Chinese Version of the MSAS
multi-dimensional nature of pain, including intensity of pain (four single items) and subsequent interference with life activities (seven items) in the preceding 24 hours. Each item is rated on a scale of 0 (‘‘no pain’’) to 10 (‘‘the worst I can imagine’’).19 For this study, a single item of worst pain was used to yield the severity score. An interference score, representing the average of the seven items, was computed.
Validation Procedures There were two phases in the validation procedures. In the first, forward and backward translations were undertaken, as well as expert evaluation of the content relevance and semantic equivalence and a pre-testing. In the second, psychometric evaluation was performed. Phase I. The translation of the MSAS into Chinese involved a linguistics expert and three bilingual investigators. In the first place, an independent forward translation of the original English version of the MSAS into Chinese was performed by a linguistics expert and a bilingual investigator, to make the wording and expressions of the items culturally accessible to people in the Hong Kong community. The next step was to make a back-translation into English, using two other bilingual investigators who had not seen the original English version. The forward and backward translations were reviewed and reconciled until an acceptable Chinese translation was reached that best reflected the linguistic and conceptual matter of the original English MSAS. Content validity of the MSAS-Ch was assessed by six experts to rate each item as a relevant element for the target construct within the local culture (content relevance), and to rate each item as conceptually and idiomatically same meaning with the English version (semantic equivalence).21,22 A four-point rating scale was used to assess each of these parameters: 1, not relevant, 2, somewhat relevant, 3, relevant and 4, very relevant. The content validity index (CVI) of content relevance and semantic equivalence for each item and total items were calculated from the proportion of experts who scored items as either 3 or 4.23 Possible CVI scores can range from 0 to 1, and a minimum of 0.80 was adopted for this study.23
47
The MSAS-Ch was pre-tested with 10 cancer patients from an outpatient clinic in an oncology department. All of the subjects were asked to complete the MSAS-Ch and they were then interviewed to determine whether they found the questions to be appropriate and comprehensible. Phase II. In order to determine its psychometric properties, 370 patients from an outpatient clinic, and in the wards in the same oncology department as used for the pre-test, were asked to complete the MSAS-Ch to test its reliability and validity. The statistical analysis began by an assessment of the pattern of missing values among the responses to MSAS-Ch items. A patient was excluded from psychometric evaluation if responses were not given to more than 13% of the items in the MSAS-Ch, as in the validation study of the original MSAS.2 For the purposes of this calculation, severity, frequency and distress were each considered as an item. For patients who had less than 13% of missing items, a simple method for imputing items with reference to the original work was adopted.2 By this method, if at least half of the dimensions in a symptom have been answered, the equation given above for calculating the symptom score is applied, ignoring any dimensions with missing values when making the calculation. Conversely, if more than half of the dimensions in a symptom have not been answered, the symptom score is recorded as missing and thus not included in the calculation of symptom score. The degree of variability among the individual items was assessed using summary statistics. In addition, floor and ceiling effects based on item-descriptive statistics were determined. The floor effect refers to a high percentage of subjects scoring the lowest possible score (not at all), while the ceiling effect refers to a high percentage of subjects achieving the highest possible score (very much).24 A percentage of 20% at the floor or at the ceiling was considered to be a significant effect. Likewise, a percentage of 70% was considered to be a high floor or ceiling, indicating that the scale is limited in its responsiveness to clinical change.24 The individual and total scores for each item were calculated in the same way as in the validation study of the original MSAS. To assess
48
Cheng et al.
structural validity of the MSAS-Ch, confirmatory factor analysis (CFA) using LISREL 8.50 for Windows (Scientific Software International Inc., Lincolnwood, IL) was performed to examine whether the hypothesized factor structure from exploratory factor analysis in the original study adequately fit our present sample data. CFA was used by entering the correlation matrix and using the unweighted least squares estimation procedure.25 The goodness-of-fit indices that we used to assess the fit of each model in this study included (a) the comparative fit index (CFI), (b) the goodness-of-fit index (GFI), (c) the root mean square error of approximation (RMSEA),26 (d) the normed fit index (NFI), (e) the nonnormed fit index (NNFI), and (f) the relative fit index (RFI).27,28 The RMSEA has the advantage of being robust with respect to increase in degree of freedom, and the latter three indices have the advantage of being robust with respect to sample size increases.29 The goodness-of-fit criteria for each index are as follows: CFI, GFI, NFI, NNFI, and RFI >0.9;30 and RMSEA <0.06.31 Cronbach’s alpha coefficient was used to assess the internal consistency reliability. An alpha within the range of 0.70e0.95 was accepted as satisfactory for internal consistency.32,33 For the test-retest reliability testing, 61 hospitalized subjects who had completed the MSAS-Ch were randomly selected and asked to complete the instrument again within 24e48 hours.34,35 The 24e48 hours was judged to be the optimum retest interval; this would be sufficiently long for subjects to forget their initial responses to the items, but not so long that the severity of symptoms would change substantially.34,35 The test-retest analysis of the subscale and total scores was performed using intraclass correlation (ICC). All values greater than 0.70 for ICC were accepted as a satisfactory level for test-retest reliability.36 Concurrent validity was estimated from the correlations between the MSAS-Ch and the FACT-G (Ch) subscales. It was predicted that there would be moderate correlation between MSAS-Ch and relevant FACT-G (Ch) subscales, indicating that they assessed related but different outcome constructs. The convergent validity of the MSAS-Ch was tested by correlating its subscale and total scores with the pain severity and pain interference subscale scores on the
Vol. 37 No. 1 January 2009
BPI-C, as well as with the average total score of the SDS-C. If the correlation coefficient is between 0.4 and 0.8, convergent validity is established.33 Known-group validity was tested by comparing the MSAS-Ch subscale and total scores with the performance status by four different KPS categories (KPS 20e50%; 60e70%; 80%; and 90e100%), and hospitalization status (inpatient and outpatient).
Results Phase I Expert review of the MSAS-Ch was undertaken by two clinical oncologists (33.3%), three oncology nurse specialists (50%), and an advanced practicing nurse in oncology (16.7%). Five of these (83.3%) were female. Results showed that the content relevancy and semantic equivalence CVI for each item ranged from 0.83 to 1. The total 32-item MSAS-Ch had a content relevancy CVI of 0.94 and a semantic equivalence CVI of 0.97, both of which were greater than the predetermined acceptable CVI level of 0.80. A pre-test was conducted with 10 patients (six female) with a mean SD age of 42 13 years (range 21e58 years), and 60% with secondary education. The majority of them were diagnosed with nasopharyngeal cancer or breast cancer (70%). The subjects in the pre-test did not report any significant problem with item comprehension and identified no culturally irrelevant item in the MSAS-Ch.
Phase II The MSAS-Ch (Appendix) was administered to 370 patients with cancer between 2003 and 2005. The proportion of missing responses per item (one of the three dimensions in a symptom) was low, with 2% of patients missing one dimension in a symptom. None of the patients had two dimensions or more missing in a symptom. All of the patients were included in the psychometric analysis after imputing missing values. As shown in Table 1, the mean SD age of the patients was 54.2 11.9 years (range 21e84 years), 193 (52.2%) were females and 56% had secondary or tertiary education. Twenty-four percent were diagnosed with head and neck cancer (n ¼ 88), 22% with breast cancer (n ¼ 80), and 21% with colorectal cancer (n ¼ 76). Most of the patients were
Vol. 37 No. 1 January 2009
Validation of the Chinese Version of the MSAS
Table 1 Demographic and Clinical Characteristics (n¼370) Characteristics
Mean (SD)
Age (years) Duration since diagnosis (months)
54.2 (11.9) 23.5 (30.3)
Gender Male Female
f (%) 177 (47.8) 193 (52.2)
Education level No formal education Primary Secondary Tertiary Unknown
29 126 175 31 9
(7.8) (34.1) (47.3) (8.4) (2.4)
88 80 76 53 28 45
(23.8) (21.6) (20.5) (14.3) (7.6) (12.2)
Stage of cancer I II III IV Unknown
43 78 105 128 16
(11.6) (21.1) (28.4) (34.6) (4.3)
Cancer therapy Chemotherapy Radiotherapy Chemo-radiotherapy
175 (47.3) 78 (21.1) 117 (31.6)
Completion of cancer treatment No Yes
214 (57.8) 156 (42.2)
Treatment setting Outpatient Inpatient
255 (68.9) 115 (31.1)
Site of cancer Head and neck Breast Colorectal Lung Gynecological Others
in Stages III or IV of the disease (63%). More than half were receiving active cancer treatment (n ¼ 247, 58%), and 68.9% (n ¼ 255) were in an outpatient treatment setting. The mean SD and median (range) number of symptoms experienced was 14 3.89 and 13 (8-33). As shown in Table 2, the symptom prevalence was moderate, ranging from 57.3% for dry mouth to 8.9% for problems with urination. The six most prevalent symptoms reported by the patients were dry mouth (57.3%), lack of energy (54.1%), worrying (43.2%), pain (42.4%), change in the way food tastes (39.7%) and difficulty sleeping (39.7%). The highest symptom score was associated with ‘‘pain’’ and ‘‘lack of appetite,’’ with a mean of 2.4. The majority (22 of 32 items) of the symptom scores were >2. Table 2 also summarizes the MSAS-Ch data at the level of
49
each item. All of the items showed a noteworthy degree of variation in frequency, severity and distress. A floor effect (>20% of scores at the lowest possible score) was shown for 12, 32 and 20 of the 32 items in terms of frequency, severity and distress, respectively. By contrast, only 7, 1 and 0 of the 32 items in terms of frequency, severity and distress, respectively, had a ceiling effect (>20% of scores at the highest possible score). A high floor or ceiling effect (>70% of scores at the lowest possible score) was not observed for any of the items in the MSAS-Ch. Referencing the results of the original study,2 a two-factor structure of a hypothesized model of the MSAS-Ch was specified. The overall fit of the data-model with the MSAS-Ch was then examined by the goodness-of-fit indices. The CFA confirmed the two latent factors generated in the original sample of Portenoy et al.:2 PHYS (Factor 1) and PSYCH (factor 2), for the observed data (Fig. 1). In addition, the CFA proved that this two-factor model adequately fit our present sample data. The corresponding goodness-of-fit indices of CFI, GFI, NFI, NNFI and RFI were all above 0.95, and the RMSEA was 0.055, which is less than 0.06 and indicates good fit of the two-factor model of the MSAS-Ch. The internal consistencies of the subscale and total MSAS-Ch were similar to those in the original work.2 The Cronbach’s alpha coefficients of the PHYS (Factor 1) and PSYCH (Factor 2) subscales were 0.79 and 0.81, respectively. The total scale Cronbach’s alpha coefficient for all 32 items (TMSAS-Ch) was high at 0.87. Thirteen of 18 corrected item-total correlations were higher than 0.40, indicating that the items were moderately correlated with the respective subscales. Table 3 also shows that the alpha coefficients, if items were deleted, were comparable to the overall alpha coefficient for each of the two subscales except for Items 1, 10, and 11. To assess the reliability of the MSAS-Ch over time, 61 respondents were selected at random to fill in the MSAS-Ch again within 24e48 hours. The testretest ICC for the PHYS subscale was 0.68, which was at the borderline value for the criterion of test-retest reliability. The test-retest ICC for the PSYCH subscale and TMSAS-Ch were 0.79 and 0.74, separately, all of which were in excess of 0.70.
50
Table 2 MSAS-Ch Item-Level Summary Statistics (n¼370) % Distribution on Response Categories Prevalence Item MSAS-Ch1 MSAS-Ch2 MSAS-Ch3 MSAS-Ch4 MSAS-Ch5 MSAS-Ch6 MSAS-Ch7 MSAS-Ch8 MSAS-Ch9
MSAS-Ch20 MSAS-Ch21 MSAS-Ch22 MSAS-Ch23 MSAS-Ch24 MSAS-Ch25 MSAS-Ch26
Average of Frequency, Severity and Distress Scores
Distress
1
2
3
4
1
2
3
4
0
1
2
3
4
Mean (SD)
134 157 200 131 115 212 106 137 126
(36.2) [40.1] (42.4) [63.1] (54.1) [73.4] (35.4) [29.5] (31.1) [62.4] (57.3) [55.3] (28.6) [44.7] (37) [59.7] (34.1) [36.4]
12.7 8.9 12.5 26.0 22.6 9.9 33.0 14.6 21.4
59.0 42.0 45.0 52.7 50.4 42.5 45.3 42.3 41.3
17.9 23.6 31.5 15.3 18.3 27.4 20.8 30.7 17.5
10.4 25.5 11.0 6.1 8.7 20.3 0.9 12.4 19.8
22.7 25.6 24.6 51.5 43.0 26.7 47.1 31.1 45.2
62.1 40.4 50.8 33.1 38.6 47.1 37.3 43.7 30.2
12.1 23.1 20.6 12.3 13.2 20.0 13.7 22.2 20.6
3.0 10.9 4.0 3.1 5.3 6.2 2.0 3.0 4.0
16.9 16.0 25.6 40.5 21.1 33.7 19.0 42.5 37.1
50.8 24.4 34.2 30.5 36.8 30.8 48.0 21.6 32.3
18.5 28.2 22.6 20.6 22.8 15.9 23.0 23.1 15.3
10.8 19.2 13.6 6.1 12.3 14.9 8.0 9.7 12.1
3.1 12.2 4.0 2.3 7.0 4.8 2.0 3.0 3.2
2.0 2.4 2.1 1.8 2.0 2.2 1.8 2.0 2.0
(0.6) (0.8) (0.7) (0.7) (0.8) (0.8) (0.7) (0.7) (0.8)
147 68 33 50 110 57 139 97 160 38
(39.7) [52.8] (18.4) [38.7] (8.9) [15.6] (13.5) [21.1] (29.7) [22.9] (15.4) [23.9] (37.6) [67.4] (26.2) [a] (43.2) [72.4] (10.3) [23.3]
12.2 13.4 18.2 38.0 27.3 28.1 22.3 14.4 14.4 21.1
46.9 44.8 39.4 52.0 46.4 52.6 42.4 44.3 53.1 26.3
24.5 19.4 9.1 10.0 17.3 15.8 25.2 34.0 20.6 7.9
16.3 22.4 33.3 0.0 9.1 3.5 10.1 7.2 11.9 44.7
25.5 32.4 54.8 30.0 39.1 38.6 32.8 28.9 30.2 23.7
44.1 39.7 35.5 52.0 46.4 52.6 40.1 37.1 44.7 34.2
20.0 23.5 3.2 16.0 11.8 7.0 21.2 28.9 17.0 21.1
10.3 4.4 6.5 2.0 2.7 1.8 5.8 5.2 8.2 21.1
22.8 22.1 33.3 18.4 21.8 29.8 11.7 41.2 12.6 47.4
31.7 35.3 46.7 38.8 45.5 43.9 32.8 28.9 34.6 28.9
21.4 17.6 6.7 26.5 18.2 15.8 29.9 9.3 28.9 15.8
13.1 20.6 10.0 14.3 10.0 7.0 18.2 18.6 14.5 7.9
11.0 4.4 3.3 2.0 4.5 3.5 7.3 2.1 9.4 0.0
2.2 2.2 2.0 1.9 1.9 1.8 2.2 2.0 2.2 2.2
(0.8) (0.8) (0.8) (0.6) (0.7) (0.6) (1.0) (0.7) (0.8) (0.8)
91 132 103 79 129 89 147
(24.6) (35.7) (27.8) (21.4) (34.9) (24.1) (39.7)
20.9 13.6 31.1 20.3 15.5 d d
50.5 37.1 58.3 32.9 55.0 d d
17.6 24.2 9.7 22.8 12.4 d d
11.0 25.0 1.0 24.1 17.1 d d
41.8 21.7 53.4 22.8 33.6 32.6 30.6
39.6 42.6 35.9 39.2 39.8 38.2 37.4
13.2 21.7 10.7 21.5 18.0 16.9 19.7
5.5 14.0 0.0 16.5 8.6 12.4 12.2
31.9 15.5 29.4 16.5 17.2 17.0 17.9
36.3 31.0 44.1 31.6 38.3 34.1 39.3
16.5 20.2 19.6 22.8 21.9 23.9 24.8
12.1 20.2 5.9 17.7 13.3 17.0 9.0
3.3 13.2 1.0 11.4 9.4 8.0 9.0
1.9 2.4 1.7 2.3 2.1 2.1 2.1
(0.8) (0.9) (0.6) (0.9) (0.8) (0.9) (0.9)
137 139 81 43 84 121
(37) [27] (37.6) [17.1] (21.9) [33.6] (11.6) [27.5] (22.7) [28.2] (32.3) [a]
d d d d d d
d d d d d d
d d d d d d
d d d d d d
42.3 32.4 29.6 44.2 41.7 37.2
32.8 23.7 39.5 34.9 32.1 43.8
18.2 27.3 22.2 16.3 15.5 15.7
6.6 16.5 8.6 4.7 10.7 3.3
30.1 34.1 21.5 23.8 14.5 35.8
30.9 26.7 36.7 38.1 34.9 29.2
22.1 20.7 22.8 23.8 24.1 24.2
10.3 11.1 11.4 9.5 14.5 7.5
6.6 7.4 7.6 4.8 12.0 3.3
1.9 2.1 2.0 1.8 2.1 1.8
(0.8) (0.9) (0.8) (0.8) (0.9) (0.7)
[27.8] [44.5] [23.5] [10.9] [47.2] [12.9] [37.2]
Numbers in [ ] represent prevalence (%) of the item in the original MSAS. a Not reported in the original MSAS.
Vol. 37 No. 1 January 2009
MSAS-Ch27 MSAS-Ch28 MSAS-Ch29 MSAS-Ch30 MSAS-Ch31 MSAS-Ch32
Severity
Cheng et al.
MSAS-Ch10 MSAS-Ch11 MSAS-Ch12 MSAS-Ch13 MSAS-Ch14 MSAS-Ch15 MSAS-Ch16 MSAS-Ch17 MSAS-Ch18 MSAS-Ch19
Difficulty concentrating Pain Lack of energy Cough Feeling nervous Dry mouth Nausea Feeling drowsy Numbness/tingling in hands/feet Difficulty sleeping Feeling bloated Problems with urination Vomiting Shortness of breath Diarrhea Feeling sad Sweats Worrying Problems with sexual interest or activity Itching Lack of appetite Dizziness Difficulty swallowing Feeling irritable Mouth sores Change in the way food tastes Weight loss Hair loss Constipation Swelling of arms or legs ‘‘I don’t look like myself’’ Changes in skin
f (%)
Frequency
Vol. 37 No. 1 January 2009
0.58
MSAS-Ch2
0.70
MSAS-Ch3
0.65
0.68
MSAS-Ch6
0.54
0.79
MSAS-Ch7
0.56
0.80
MSAS-Ch8
0.95
MSAS-Ch11
0.69
MSAS-Ch13
0.58
MSAS-Ch21
0.93
MSAS-Ch22
0.47
0.78
MSAS-Ch26
0.55
0.70
MSAS-Ch27
0.45
0.79
MSAS-Ch29
0.73
MSAS-Ch1
0.59
MSAS-Ch5
0.72
MSAS-Ch10
0.53
0.41
MSAS-Ch16
0.77
0.44
MSAS-Ch18
0.69
MSAS-Ch24
Validation of the Chinese Version of the MSAS
0.46 0.45 0.21 0.33
PHYS
0.65 0.27
0.69
0.52 0.64
PSYCH
0.75 0.71
Fig. 1. Confirmatory factor analysis (n ¼ 370).
The mean SD of the PHYS, PSYCH, GDI, and TMSAS-Ch scores were 1.93 0.57, 2.01 0.64, 1.87 0.73, and 1.91 0.52, respectively. Pearson cross-correlations for the MSAS-Ch and the validation measures are shown in Table 4. As expected, the MSAS-Ch PHYS subscale showed the highest correlation with the FACT-G (Ch) Phy subscale (r ¼ 0.62, P < 0.01) relative to other FACT-G (Ch) subscale scores. The PSYCH subscale also correlated moderately with the FACT-G (Ch) Emt subscale (r ¼ 0.51, P < 0.01), Phy subscale (r ¼ 0.58, P < 0.01), and total score (r ¼ 0.53, P<0.01). In addition, the TMSASCh correlated moderately with the FACT-G (Ch) total score (r ¼ 0.58, P < 0.01), but its highest correlation was with the FACT-G (Ch) Phy subscale (r ¼ 0.65, P < 0.01), supporting the concurrent validity. The results were consistent with those reported in the original version.2 The Pearson correlation coefficients between scores on the MSAS-Ch PHYS and SDS total were 0.53 (P < 0.01) and between
51
the MSAS-Ch PSYCH and SDS total were 0.49 (P < 0.01), revealing a satisfactory convergent validity. The MSAS-Ch PHYS subscale (r ¼ 0.49, P < 0.01) and total (r ¼ 0.46, P < 0.01) scores correlated moderately with the severity score on the BPI-Ch. To replicate the original MSAS,2 Pearson cross-correlations for the MSAS-Ch GDI and the validation measures were computed. As shown in Table 4, the MSAS-Ch GDI was moderately correlated with the FACT-G (Ch) (r ¼ 0.55e0.64, P < 0.01) except for the Soc/Fam and Doc subscales, and with the SDS total score (r ¼ 0.56, P < 0.01). This result indicates that the MSAS-Ch did measure the symptom distress dimension in the same ways as the original MSAS.2 The mean SD KPS score was 90.35 11.24 (median 90; range 50e100); 89.7% of the subjects had a KPS score $80. Owing to the small number of subjects who were rated ‘‘20e50’’ on KPS (n ¼ 4, 1.1%), these were pooled into the ‘‘60e70’’ category. As shown in Table 5, patients with high performance status (KPS) had a lower score on the MSAS-Ch subscales and total than the subjects with poor performance status. The Kruskal-Wallis test showed that the MSAS-Ch subscales and total scores were significantly different using the KPS (P < 0.01), supporting the known group validity. Post hoc analysis using Mann-Whitney U tests with Bonferroni correction showed that subjects with a KPS score of 90e100 reported significantly lower scores on the MSAS-Ch subscales and total than those subjects with KPS scores of 80 (P < 0.01) and 50e70 (P < 0.01). As for the treatment setting, inpatients had a higher score on the MSAS-Ch subscales and total than the outpatients (P < 0.01), revealing a known group validity in which MSAS-Ch was able to differentiate between subjects according to their hospitalization status (Table 6). These results were consistent with those reported in the original version.2
Discussion Although MSAS has been developed in English-speaking cultures and applied in various settings, such as in patients with advanced and terminal cancer in the palliative care setting, noncancer hospitalized patients near the end of life, and the pediatric oncology setting, it is not clear whether the MSAS can also
52
Cheng et al.
Vol. 37 No. 1 January 2009
Table 3 Reliability Analysis of the MSAS-Ch (n ¼ 370) Internal Consistency Item
Squared Multiple Correlation
Corrected Item-Total Correlation
PHYS MSAS-Ch26 Change in the way food tastes MSAS-Ch27 Weight loss MSAS-Ch7 Nausea MSAS-Ch13 Vomiting MSAS-Ch8 Feeling drowsy MSAS-Ch21 Lack of appetite MSAS-Ch3 Lack of energy MSAS-Ch2 Pain MSAS-Ch6 Dry mouth MSAS-Ch11 Feeling bloated MSAS-Ch22 Dizziness MSAS-Ch29 Constipation
0.365
0.491
0.430 0.386 0.347 0.318 0.426 0.339 0.339 0.265 0.076 0.098 0.213
0.544 0.437 0.385 0.437 0.611 0.488 0.488 0.476 0.180 0.255 0.388
PSYCH MSAS-Ch24 MSAS-Ch5 MSAS-Ch16 MSAS-Ch18 MSAS-Ch10 MSAS-Ch1
0.414 0.413 0.513 0.528 0.187 0.156
0.623 0.615 0.694 0.693 0.431 0.387
Feeling irritable Feeling nervous Feeling sad Worrying Difficulty sleeping Difficulty concentrating
be applied in other cultural contexts and oncology settings. This study revealed a high prevalence of certain symptoms in patients during cancer therapy or during the early posttreatment period, particularly dry mouth, lack of energy, worrying, and pain. This finding is similar to the findings of previous studies with various cancer populations and oncology settings.1e3,9,37 Similar to Tranmer et al., the highest symptom score was associated with pain, and for the majority of symptoms, if Table 4 Correlations for the MSAS-Ch and the Validation Measures (n¼370) MSAS-Ch
FACT-G(Ch) Phy Soc/Fam Doc Emt Fnt Total SDS-Ch Total BPI-Ch Pain severity (n¼157) Pain interference (n¼157) a
P<0.01.
PHYS
PSYCH
GDI
TMSAS
0.620a 0.153a 0.104 0.409a 0.443a 0.527a
0.578a 0.111 0.007 0.510a 0.432a 0.525a
0.643a 0.222a 0.153a 0.564a 0.547a 0.637a
0.650a 0.198a 0.106 0.507a 0.483a 0.580a
0.529a
0.485a
0.560a
0.582a
0.487a
0.294a
0.346a
0.459a
0.304a
0.232
0.392a
0.320a
Cronbach Alpha if Alpha Item Deleted 0.795
0.775 0.771 0.781 0.787 0.781 0.761 0.776 0.776 0.777 0.802 0.795 0.785
0.810
0.769 0.773 0.750 0.751 0.813 0.817
present, the patients reported that they were frequent, moderately severe to severe, and somewhat to considerably distressing.9 The present study demonstrates that the Chinese version of the MSAS has adequate psychometric properties of internal consistency, concurrent and convergent validity, and known group validity when applied to Hong Kong Chinese patients with cancer, and that these values are equivalent to those of the original scale. CFA confirmed the construct validity of the MSAS-Ch, with a good fit between the hypothesized model and the observed data. Translation and adaptation of English-speaking cultures’ instruments has been a trend around the world. Linguistic and conceptual equivalences, as well as content relevance taking cultural characteristics into account, are fundamental requirements of all translated instruments and are prerequisite for establishing other types of validity. In this study, a rigorous multistepped translation process was undertaken in developing the MSAS-Ch. The expert content review revealed a high degree of agreement among seven experts in terms of content relevancy and semantic equivalence, indicating that the meanings of the items were the same after translation into Chinese and the items were relevant within the Hong Kong Chinese culture, thereby revealing the
Vol. 37 No. 1 January 2009
Validation of the Chinese Version of the MSAS
53
Table 5 Difference in MSAS-Ch Subscale Scores According to KPS KPS 50e70 (n¼38)
KPS 80 (n¼58)
KPS 90e100 (n¼274)
MSAS-Ch Mean
SD
Median
Range
Mean
SD
Median
Range
Mean
SD
Median
Range
c2
P-value
PHYS PSYCH GDI TMSAS
0.53 0.69 0.59 0.53
2.40 2.30 2.10 2.29
0.93e3.48 1.53e3.94 1.27e3.90 1.31e3.50
2.23 2.23 2.22 2.16
0.59 0.64 0.64 0.51
2.09 2.16 2.13 2.07
1.38e3.64 1.27e4.00 0.80e3.60 1.48e3.66
1.79 1.88 1.68 1.78
0.51 0.59 0.73 0.46
1.76 1.77 1.60 2.07
0.90e3.58 0.93e4.00 0.80e3.53 0.93e3.20
51.3 25.3 62.6 52.0
<0.01 <0.01 <0.01 <0.01
2.38 2.42 2.55 2.36
content validity of the MSAS-Ch. In addition, the factor structure of original MSAS was found to have cross-cultural comparability, in which the two latent factors being identified in American populations could adequately fit our present sample data. Using the variety of fit statistics in CFA, the results were convincing and confirmed the construct validity of the MSAS-Ch, with a good fit between the hypothesized model and our present sample data. Nevertheless, it is noteworthy that two items, MSAS-Ch 11 (Feeling bloated) and 22 (Dizziness) were weakly loaded onto the PHYS subscale, unlike the loading reported by Portenoy et al.2 A similar weak factor loading of the ‘‘Feeling bloated’’ item on PHYS was also reported by Tranmer and colleagues.9 One probable explanation is less prevalence of feeling bloated in our study sample compared with the sample for the original study. Another possible reason could be the low symptom score yielded in dizziness in our present sample. Nevertheless, there is uncertainty about whether the prevalence of symptoms or the constellation of shared dimensions of symptoms encompassing frequency, intensity, and level of perceived distress would affect the strength of the factor loading. Further work using Rasch analysis is needed to complement the traditional psychometric evaluation of validity to permit a detailed examination of the structure of MSAS-Ch. The MSAS-Ch PHYS and PSYCH subscales had a high degree of internal consistency, thus supporting their reliability. In addition,
the testeretest reliabilities were good, meeting the ICC coefficient requirements. The relative low value of ICC for the PHYS subscale was possibly due to change of some physical symptoms within 24e48 hours. In comparison with the original validation (0.84e0.88),2 the MSAS-Ch subscale alpha coefficients were relatively low. In particular, the alpha coefficient for the MSAS-Ch PHYS (0.79) was lower than that for the coefficient obtained in the original study (0.88). Notably, the coefficients of corrected item-total correlation of ‘‘Feeling bloated’’ (0.18) and ‘‘Dizziness’’ (0.26) were very low, which appeared to affect the interitem correlation structure and thus the internal consistency of the MSAS-Ch PHYS subscale. In addition, differences in the symptom prevalence between the present and the original study may contribute to the relatively low alpha coefficient of MSAS-Ch PHYS. In the study of Portenoy et al., the PHYS subscale contained 12 symptoms that were relatively prevalent in their sample.2 Those patients reported a higher prevalence of pain, lack of energy, nausea and vomiting, feeling drowsy and bloated, lack of appetite and constipation, as compared with our study sample. There is some uncertainty about whether the prevalence of symptoms would affect the interitem correlation structure and thus the internal consistency of the subscale. The subscale scores of MSAS-Ch were moderately correlated with the scores on various validation measures that assessed psychological distress, pain, and HRQoL, reflecting that
Table 6 Differences in MSAS-Ch Subscale Scores According to the Hospital Setting Inpatient Setting (n¼119)
PHYS PSYCH GDI TMSAS
Outpatient Setting (n¼251)
Mean
SD
Median
Range
Mean
SD
Median
Range
z
P-value
2.07 2.10 2.09 2.05
0.58 0.63 0.71 0.52
1.98 1.87 2.00 1.99
0.90e3.58 0.93e4.00 0.80e3.90 0.93e3.20
1.86 1.95 1.75 1.84
0.55 0.65 0.71 0.50
1.77 1.84 1.68 1.77
0.90e3.64 1.13e3.94 0.80e3.53 1.22e3.66
3.3 2.096 3.956 3.208
<0.01 0.036 <0.01 <0.01
54
Cheng et al.
they were measuring a similar construct. These findings were coherent and comparable to those in the original study.2 Construct validity was also supported by comparing the MSASCh scores in subpopulations that varied clinically. Inpatients and patients with poorer performance status scored higher on the MSAS subscale and total scores than outpatients and patients with higher performance status. The floor effect for the sample was modest, whereas the ceiling effect was negligible. One possible explanation for the floor effect is that the majority of the patients in this sample had only mildly distressing symptoms and so would be expected to score at the floor level. Nevertheless, further evaluation of the floor and ceiling effects is warranted, to include a larger population with severely distressing symptoms. In addition, further research should be conducted using item response analysis to investigate the performance of individual item response choices.
Conclusion The MSAS-Ch has demonstrated an acceptable level of psychometric properties, and cross-cultural equivalence with the original version, as a multidimensional instrument that measures the prevalence and burden of common symptoms in cancer patients. The MSAS-Ch would provide a common platform for use by clinicians to assess physical and psychological symptoms during cancer therapy and at the early post-treatment stage in Chinese-speaking patients. In addition, the MSAS-Ch could facilitate the exploration of the effectiveness of symptom management strategies. Nevertheless, instrument validation is by no means a one-time event. In future studies, an attempt should be made to determine if certain items in MSAS-Ch behave differently across cancer diagnostic groups and the disease trajectory based on modern psychometric evaluation of differential item functioning. Rasch analysis is also highly recommended so as to enable a detailed examination of the structure of the MSAS-Ch.
Vol. 37 No. 1 January 2009
the early stages of the study. Likewise, they are also grateful to the oncologists and nurses for their assistance with the content validation. Finally, they wish to express their gratitude to the patients for agreeing to participate in this study.
References 1. Naughton M, Homsi J. Symptom assessment in cancer patients. Curr Oncol Rep 2002;4:256e263. 2. Portenoy RK, Thaler HT, Kornblith AB, et al. The Memorial Symptom Assessment Scale: an instrument for the evaluation of symptom prevalence, characteristics and distress. Eur J Cancer 1994; 30A(9):1326e1336. 3. Chang VT, Hwang SS, Feuerman M, et al. Symptom and quality of life survey of medical oncology patients at a Veterans Affairs Medical Center: a role for symptom assessment. Cancer 2000;88: 1175e1183. 4. Donnelly S, Walsh D, Rybicki L. The symptoms of advanced cancer: identification of clinical and research priorities by assessment of prevalence and severity. J Palliat Care 1995;11(1):27e32. 5. Ruthledge DM, McGuire C. Evidence-based symptom management. In: Yarbro CH, Frogge MH, Goodman M, eds. Cancer symptom management, 3rd ed. Sudbury, MA: Jones & Bartlett, 2003. 6. Paice JA. Assessment of symptom clusters in people with cancer. J Nat Cancer Instit Mono 2004;32:98e102. 7. Chang VT, Hwang SS, Feuerman M. Validation of the Edmonton Symptom Assessment Scale. Cancer 2000;88:2164e2171. 8. Portenoy RK, Thaler HT, Kornblith AB, et al. Symptom prevalence, characteristics and distress in a cancer population. Qual Life Res 1994;3: 183e189. 9. Tranmer JE, Heyland D, Dudgeon D, et al. Measuring the symptom experience of seriously ill cancer and noncancer hospitalized patients near the end of life with the Memorial Symptom Assessment Scale. J Pain Symptom Manage 2003;25:420e429. 10. Armstrong TS. Symptoms experience: a concept analysis. Oncol Nurs Forum 2003;30(4):601e606. 11. Alexander J. Symptom management. In: Gukkatte MM, ed. Clinical guide to antineoplastic therapy: a chemotherapy handbook. Pittsburgh, PA: Oncology Nursing Society, 2001: 297e313.
Acknowledgments
12. Brescia FJ, Adler D, Gray G, et al. Hospitalized advanced cancer patients: a profile. J Pain Symptom Manage 1990;5:221e227.
The authors’ special thanks go to Mr. Bernard Yam for his exemplary assistance during
13. Coyle N, Adelhardt J, Foley KM, Portenoy RK. Character of terminal illness in the advanced cancer
Vol. 37 No. 1 January 2009
Validation of the Chinese Version of the MSAS
patient: pain and other symptoms during the last four weeks of life. J Pain Symptom Manage 1990;5: 85e93. 14. Kutner JS, Bryant LL, Beaty BL, et al. Time course and characteristics of symptom distress and quality of life at the end of life. J Pain Symptom Manage 2007;5:85e93. 15. Vainio A, Auvinen A. Prevalence of symptoms among patients with advanced cancer: an international collaborative study. J Pain Symptom Manage 1996;12(1):3e10. 16. Collins JJ, Devine TD, Dick GS, et al. The measurement of symptoms in young children with cancer: the validation of the Memorial Symptom Assessment Scale in children aged 7e12. J Pain Symptom Manage 2002;23(1):10e16. 17. Yu CLM, Fielding R, Chan CLW, et al. Measuring quality of life of Chinese cancer patients: a validation of the Chinese version of the functional assessment of cancer therapy-general (FACT-G) scale. Cancer 2000;88:1715e1727. 18. Chan WH. The Chinese translation of the revised Piper Fatigue Scale and Symptom Distress Scale. Research Report, . The Chinese University of Hong Kong. Hong Kong: The Nethersole School of Nursing, 2000. 19. Wang XS, Mendoza TR, Gai S, Cleeland CS. The Chinese version of the Brief Pain Inventory (BPI-C): its development and use in a study of cancer pain. Pain 1996;67:407e416. 20. Yates JW, Chalmer B, McKegney FP. Evaluation of patients with advanced cancer using the Karnofsky Performance Status. Cancer 1980;40: 2222e2224.
55
size in confirmatory factor analysis models. In: Gaul W, Schader M, eds. Classification as a tool of research. Amsterdam: Elsevier Science Publishers, 1986: 3e10. 26. Steiger JH. Changing causal relationships without changing the fit: some rules for generating equivalent LISREL models. Multivariate Behav Res 1990;21:309e331. 27. Tucker L, Lewis C. A reliability coefficient for maximum likelihood factor analysis. Psychometrika 1973;38:1e10. 28. Bentler PM, Bonett DG. Significance tests and goodness of fit in the analysis of covariance structures. Psychol Bull 1980;88:588e606. 29. MacCallum RC, Hong S. Power analysis in covariance structure modeling using GFI and AGFI. Multivariate Behav Res 1997;32:193e210. 30. Marsh HW, Balla JR, Hau KT. An evaluation of incremental fit indices: a clarification of mathematical and empirical properties. In: Marcoulides GA, Schumacker RE, eds. Advanced structural equation modelling: Issues and techniques. Hillsdale, NJ: Erlbaum Publishers, 1996: 315e535. 31. Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equation Model 1999;6(1):1e55. 32. Pett MA, Lackey NR, Sullivan JJ. Making sense of factor analysis: The use of factor analysis in health care research. Thousand Oaks, CA: Sage Publications, 2003. 33. Nunnally JC, Bernstein IH. Psychometric theory, 3rd ed. New York: McGraw-Hill, 1994.
21. Davis LL. Instrument review: getting the most from a panel of experts. Applied Nurs Res 1992;5: 194e197.
34. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986;1:307e310.
22. Haynes SN, Richard DCS, Kubany ES. Content validity in psychological assessment: a functional approach to concepts and methods. Psychol Assessment 1995;7:238e247.
35. Sitzia J, Dikken C, Hughes J. Psychometric evaluation of a questionnaire to document side-effects of chemotherapy. J Advanced Nurs 1997;25: 999e1007.
23. Lynn MR. Determination and quantification of content validity. Nurs Res 1986;35(6):382e385.
36. Denegar CR, Ball DW. Assessing reliability and precision of measurement: an introduction to intraclass correlation and standard error of measurement. J Sport Rehab 1993;2:35e42.
24. Alexander M, Berger W, Buchholz P, et al. The reliability, validity, and preliminary responsiveness of the eye allergy patient impact questionnaire. Health Qual Life Outcomes 2005;3(1):67. 25. Gaul W, Schader M. The robustness of LISREL unweighted squares estimation against small sample
37. Homsi J, Walsh D, Rivera N, et al. Symptom evaluation in palliative medicine: patients report vs systematic assessment. Support Care Cancer 2006; 14(5):444e453.
56
Cheng et al.
Vol. 37 No. 1 January 2009
Appendix MSAS-Ch
“X
? 1.
1
2
3
4
1
2
3
4
0
1
2
3
4
2.
1
2
3
4
1
2
3
4
0
1
2
3
4
3.
1
2
3
4
1
2
3
4
0
1
2
3
4
4.
1
2
3
4
1
2
3
4
0
1
2
3
4
5.
1
2
3
4
1
2
3
4
0
1
2
3
4
6.
1
2
3
4
1
2
3
4
0
1
2
3
4
7.
1
2
3
4
1
2
3
4
0
1
2
3
4
8.
1
2
3
4
1
2
3
4
0
1
2
3
4
1
2
3
4
1
2
3
4
0
1
2
3
4
10.
1
2
3
4
1
2
3
4
0
1
2
3
4
11.
1
2
3
4
1
2
3
4
0
1
2
3
4
12.
1
2
3
4
1
2
3
4
0
1
2
3
4
13.
1
2
3
4
1
2
3
4
0
1
2
3
4
14.
1
2
3
4
1
2
3
4
0
1
2
3
4
15.
1
2
3
4
1
2
3
4
0
1
2
3
4
16.
1
2
3
4
1
2
3
4
0
1
2
3
4
17.
1
2
3
4
1
2
3
4
0
1
2
3
4
18.
1
2
3
4
1
2
3
4
0
1
2
3
4
19.
1
2
3
4
1
2
3
4
0
1
2
3
4
20.
1
2
3
4
1
2
3
4
0
1
2
3
4
21.
1
2
3
4
1
2
3
4
0
1
2
3
4
22.
1
2
3
4
1
2
3
4
0
1
2
3
4
23.
1
2
3
4
1
2
3
4
0
1
2
3
4
24.
1
2
3
4
1
2
3
4
0
1
2
3
4
9.
/
Vol. 37 No. 1 January 2009
Validation of the Chinese Version of the MSAS
"
"
57
"X"
25.
1
2
3
4
0
1
2
3
4
26.
1
2
3
4
0
1
2
3
4
27.
1
2
3
4
0
1
2
3
4
28.
1
2
3
4
0
1
2
3
4
29.
1
2
3
4
0
1
2
3
4
30
1
2
3
4
0
1
2
3
4
1
2
3
4
0
1
2
3
4
1
2
3
4
0
1
2
3
4
31." 32.
" (
)