732 Articles | JNCI Vol. 102, Issue 10 | May 19, 2010
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Cancer-related symptoms produced either by the disease itself or
by the toxicities of treatment are what patients report to clinicians
as subjective negative feelings that may be physical (such as pain,
fatigue, and shortness of breath), cognitive (such as memory prob-
lems), or affective (such as sadness and emotional distress). These
multiple symptoms collectively impose a symptom burden that
greatly affects a patient’s quality of life and daily activities (1).
Characterizing symptom burden requires accurate measure-
ment of symptom severity and the degree to which symptoms
interfere with daily life. Psychometrically validated tools for assess-
ing patient self-report of symptoms and interference (patient-
reported outcomes [PROs]) are widely accepted in oncology
practice and are increasingly used as primary or secondary out-
comes for clinical trials (2).
Psychometrically sound, culturally valid, standardized PRO
assessment tools are available for administration to patients from
diverse racial, ethnic, and cultural and language groups. However,
many PRO measures (including symptom scales) and most guide-
lines for cancer symptom management (3–6) are initially devel-
oped and validated in English and later translated into other
languages. When clinical trials that include patient self-report or
the application of treatment guidelines are conducted in patients
with diverse linguistic backgrounds, cultural differences can con-
found the accuracy and cross-similarity of the PROs, thus compli-
cating the interpretation of the trial results and the application of
the clinical guidelines. Knowing the degree to which symptom
ratings might vary as a function of language or nationality is there-
fore important for both the clinical trials and the treatment of
symptoms and requires empirical evidence of the effects of
language on the performance of a symptom measure (7).
Several well-validated PRO assessment tools are used transna-
tionally in oncology, including the Functional Assessment of
Cancer Therapy (FACT), the European Organisation for Research
and Treatment of Cancer (EORTC) QLQ-C30, and the M. D.
Anderson Symptom Inventory (MDASI) (8). Whereas the FACT
and QLQ-C30 are measures of health-related quality of life, the
MDASI focuses specifically on measuring the severity and interfer-
ence of cancer-related symptoms caused by disease and the treat-
ment process. A systematic review of cancer symptom assessment
tools by Kirkova et al. (9) rated the MDASI highly in terms of
Impact of Cultural and Linguistic Factors on Symptom
Reporting by Patients With Cancer
Xin Shelley Wang, Charles S. Cleeland, Tito R. Mendoza, Young Ho Yun, Ying Wang, Toru Okuyama, Valen E. Johnson
Manuscript received August 5, 2009; revised March 1, 2010; accepted March 2, 2010.
Correspondence to: Xin Shelley Wang, MD, MPH, Department of Symptom Research, The University of Texas M. D. Anderson Cancer Center, 1515
Holcombe Blvd, Unit 1450, Houston, TX 77030 (e-mail: email@example.com).
Background Patient reporting of the severity and impact of symptoms is an essential component of cancer symptom man-
agement and cancer treatment clinical trials. In multinational clinical trials, cultural and linguistic variations in
patient-reported outcomes instruments could confound the interpretation of study results.
Methods The severity and interference of multiple symptoms in 1433 cancer patients with mixed diagnoses and treat-
ment status from the United States, China, Japan, Russia, and Korea were measured with psychometrically
validated language versions of the M. D. Anderson Symptom Inventory (MDASI). Mixed-effect ordinal probit
regression models were fitted to the pooled data to compare the magnitude of the effect of “country” (nation
and linguistic factors) with between-subjects effects on symptom reporting, adjusted for patient and clinical
factors (age, sex, performance status, and chemotherapy status).
Results For the pooled sample, fatigue, disturbed sleep, distress, pain, and lack of appetite were the most severe
patient-reported MDASI symptoms. The magnitude of the variance of the country random effects was only one-
fourth to one-half of the interpatient variation (s2 = 0.23–0.46) for all symptoms, except nausea and vomiting.
Conclusions Cultural and linguistic variations in symptom reporting among the five language versions of the validated
MDASI were limited. Ordinal probit modeling provided a simple mechanism for accounting for cultural and
linguistic differences in patient populations. The equivalence among MDASI translations in this study suggests
that symptom ratings collected from various cultural and language groups using the MDASI can be interpreted
in a similar way in oncology practice, clinical trials, and clinical research.
J Natl Cancer Inst 2010;102:732–738
JNCI | Articles 733
flexibility, reliability and validity, ease of completion, and utility in
symptom management. The MDASI has been both linguistically
and psychometrically validated in multiple languages.
In this study, we examined the effects of language on symptom
report relative to patient and clinical factors in an analysis of
pooled data from the English (8), Chinese (10), Japanese (11),
Russian (12), and Korean (13) MDASI validation studies. We hy-
pothesized that the differences in MDASI scores attributable to
the language and culture in which the instrument was administered
would be small compared with the between-subject variation in
patients exhibiting similar demographic and clinical characteris-
tics, such as age, sex, and performance status.
This study was an analysis of data gathered in five MDASI
language validation studies. Patients in each validation study were
recruited from clinics or inpatient units at The University of Texas
M. D. Anderson Cancer Center in Houston, TX; Tianjin Medical
University Cancer Institute and Hospital, Tianjin, China; the
National Cancer Center Hospital East, Kashiwa, Chiba, Japan; four
city hospitals in St Petersburg, Russia; and five cancer centers and
university hospitals in Korea. International collaboration between
the study investigators and the Department of Symptom Research
at M. D. Anderson Cancer Center ensured that all study protocol
procedures were similar for each validation protocol. All patients
were at least 18 years old, had a pathological diagnosis of cancer,
were able to read, understand, and complete the questionnaires in
their native language, and did not have a diagnosis of severe mental
or cognitive disorder. There were no limitations as to type of can-
cer diagnosis, staging, or type or timing of cancer treatment. The
studies were approved by the institutional review boards of M. D.
Anderson Cancer Center and of the participating cancer hospitals
in each country. All patients provided consent to participate.
The MDASI (8) is a brief, psychometrically validated, multisymp-
tom assessment tool that assesses 13 symptoms commonly associ-
ated with cancer or its treatment, including pain, fatigue, nausea,
disturbed sleep, distress, shortness of breath, difficulty remem-
bering, lack of appetite, drowsiness, dry mouth, sadness, vomiting,
and numbness or tingling. Patients rate the severity of each of
these symptoms in the past 24 hours on an 11-point scale ranging
from 0 (not present) to 10 (as bad as you can imagine). Six addi-
tional items measure the degree to which symptoms have inter-
fered with various facets of the patient’s daily life during the past
24 hours, including general activity, mood, normal work (both
work outside the home and housework), relations with other
people, walking, and enjoyment of life. Interference items are also
rated on an 11-point scale ranging from 0 (does not interfere) to
10 (completely interferes).
The four non-English language versions of the MDASI used
in this analysis were developed and tested using a consistent
translation/back-translation procedure (14). The psychometric
properties for the foreign-language translations of the MDASI
included in this analysis have been shown to be satisfactory and
CONteXtS AND CAVeAtS
The results of clinical trials or treatment guidelines for patients of
different nationalities and languages may be difficult to interpret
because of linguistic and cultural differences in patient-reported
outcomes. The M. D. Anderson Symptom Inventory (MDASI) is
used to assess 13 symptoms commonly associated with cancer or
Pooled MDASI data were analyzed from validation studies con-
ducted with 1433 patients from the United States, China, Japan,
Russia, and Korea. Variations in MDASI scores attributable to lin-
guistic and cultural variations were compared with intersubject
variations in patient responses.
National and linguistic variations in patient responses to the
MDASI were small relative to individual patient-related factors.
Adjusting for patient and clinical factors, the country effect
accounted for only one-fourth to one-half of the patient-to-patient
variation in MDASI symptom severity ratings, especially for the
most severe symptoms.
Symptom data obtained using various language versions of well-
validated patient-report measures such as the MDASI can be
pooled to analyze multinational clinical research and can provide
reliable symptom assessment for oncology practice in other parts
of the world.
Only one to three treatment centers were sampled in each country,
so the effect of country may be confounded with that of individual
sample site. Cancer stage was not used as a covariate because data
for this variable from one country were missing.
From the Editors
comparable to the English version (8,10–13). The internal consis-
tency (reliability) of the symptom and interference items in each of
the five language versions of MDASI has been demonstrated by
Cronbach alpha coefficients (15), which are calculated by subtract-
ing from one the ratio of the sum of the component score variances
to the true score variance. A coefficient value lower than 0.70 sug-
gests either that one or more items have high variability or that the
items are not all measuring the same underlying construct. A high
degree of internal consistency exists in all five language versions,
with Cronbach alpha coefficients of 0.85–0.93 for the symptom
severity subscales and greater than or equal to 0.90 for the symp-
tom interference subscales.
The five countries represented the nationality and/or language
variable, defined as “country,” in our analyses. Descriptive statis-
tics, including proportions and SDs, were used to describe the
characteristics of the sample from each country. All statistical tests
for symptom severity by sex and chemotherapy status were two-
tailed, and a P value less than .05 was considered to be statistically
significant. P values reported herein represent two times the
734 Articles | JNCI Vol. 102, Issue 10 | May 19, 2010
smaller of the posterior probabilities that the parameter of interest
was either less than or greater than zero.
Ordinal probit regression modeling (16) was used to estimate
the effect of country on symptom report, with each MDASI symp-
tom score treated as an ordinal response. Country was modeled as
a random effect. The SD of the random effects attributable to
country was implicitly scaled relative to the intersubject variance of
1.0. Ordinal probit regression models allowed us to account for
subject-specific explanatory variables as fixed effects. The covariate
vectors were age, sex, Eastern Cooperative Oncology Group per-
formance status (ECOG PS) score (17), and whether or not the
patient was receiving chemotherapy at the time of assessment.
To define an ordinal probit regression model, let P
r = 0, 1, . . ., 9 denote the cumulative categorical probabilities that
patient i responds in category r or less (note that Pi,10 = 1), let b
denote a regression parameter, let xi denote a vector of patient cova-
riates for patient i, and let zi denote a five-level factor vector indi-
cating the ith patient’s culture/language. Then, the regression model
for each MDASI item can be described by the equation
, where the five components of g, gj
assumed to be random effects associated with jth culture/language,
and Φ( ) • denotes the standard normal distribution function. The
parameters ur denote category thresholds. We further assume that
the random-effects gj are independently distributed according to a
normal distribution with mean 0 and variance s2. To complete the
model specification, we assume that the prior distribution on s2 is
proportional to Cauchy density truncated to the positive real axis,
that the prior densities on the components of b are uniform on the
real line, and that the prior densities on the components of ur are
uniform subject to the constraint that u
sampling scheme to obtain a posterior sample of parameters (16).
After 2000 burn-in iterations, 100 000 updates of each parameter
were performed to obtain a joint posterior sample on all unknown
This mixed-effects ordinal probit regression modeling method
facilitated the interpretation of the country random-effects vari-
ance, s2. According to the mixed-effect probit model, the between-
patient variation for patients who have the same covariate values is
defined to be 1.0 on the latent probit scale. Thus, the magnitude
of the random-effects variance has a simple interpretation in terms
of its magnitude relative to the unit interpatient variability.
There are several reasons for modeling country effects as ran-
dom effects. First, our intent in performing these analyses was to
. We used a Gibbs
demonstrate that the MDASI can be used to assess symptoms from
many international patient populations, not just the five countries
for which data were available. Therefore, we regarded the coun-
tries available for analysis as a sample (even if not random) of the
countries that we are potentially interested in studying. Second, it
is not uncommon to fit random-effects models with four or five
random effects. We were then able to compare the “average coun-
try effect” with the effects of other variables (eg, ECOG PS).
Regarding country effects as fixed effects would have required us
to make country-specific comparisons to the effects of other vari-
ables, thus proliferating the number of comparisons. Finally,
because sufficient sample sizes from each country were included in
the analysis, the estimated country effects (the posterior mean
estimates) obtained from the random-effects model were almost
identical to those that would be obtained from the countries in the
random-effects model or from a corresponding fixed-effects
model. The shrinkage effect on the parameters associated with the
country effects that results from modeling these effects as random
was negligible in our analysis.
The focus of our study (rather than to test whether the effects
of country are zero) was to estimate the relative importance of
country on symptom reports in relation to other known factors,
such as ECOG PS. Therefore, we did not perform power calcula-
tions against prespecified alternative values of the country effect
sizes. Also, because the missing data rates were small from each
country (0.04%–2.40% on MDASI symptom items), we simply
excluded missing data from our analysis.
Data from 1433 patients were included in our analyses: 524
patients from the United States, 249 from China, 256 from Japan,
226 from Russia, and 178 from Korea. Patient demographic and
disease information is summarized in Table 1. Compared with the
other samples, the Japanese sample included fewer patients under-
going active treatment because much of the Japanese data were
collected at clinic follow-up visits (Table 1).
Symptom Severity and Prevalence
Analysis of symptom data revealed that cancer patients from the
five national and linguistic groups were similar in their symptom
experiences across the various stages of disease and treatment.
Table 1. Demographic and disease characteristics*
(n = 524)
(n = 249)
(n = 256)
(n = 226)
(n = 178)
Mean age (SD), y
Completed high school, %
Employed (full time, part time, homemaker), %
Metastatic disease, %
Good ECOG PS (0–1), %
Undergoing chemotherapy, %
MDASI missing data points, %
* N = 1433. ECOG PS = Eastern Cooperative Oncology Group performance status; MDASI = M. D. Anderson Symptom Inventory.
JNCI | Articles 735
Cross-nationally, fatigue was consistently the most prevalent
moderate to severe symptom [rated ≥5 on the MDASI 0–10 scale
(18)]. The prevalence of moderate to severe MDASI symptoms for
the pooled sample and for each nation is presented in Table 2. In
the analysis of symptom severity overall, fatigue was followed by
disturbed sleep, distress, pain, lack of appetite, and drowsiness
(Table 2). Treatment-induced symptoms such as nausea, vomit-
ing, and numbness were consistently rated as the least severe of
the 13 MDASI symptoms (Table 3).
Effect of Country on Symptom Reporting
Table 3 presents the posterior mean estimates for the mixed-
effects ordinal probit regression models that include random-ef-
fects variables to reflect the influence of cultural and linguistic
factors on symptom scores. The random-effects variance (s2) of
the country effects for each symptom and the coefficients for age,
sex, ECOG PS, and chemotherapy status can be compared with
the between-patient variance of 1.0. Positive coefficients are asso-
ciated with increased symptom severity.
The variances of random effects due to country for all symptom
ratings were less than 1.0 (Table 3). The magnitude of the variance
of the country random effects was only one-fourth to one-half,
approximately, of the interpatient variation (s2 = 0.23–0.46) for all
symptoms, except nausea and vomiting. The range of the posterior
means of the variances of the country effect was the lowest (s2 =
0.23–0.28) for fatigue, disturbed sleep, and sadness (Table 3).
Fatigue and disturbed sleep were also the most severe symptoms.
The posterior mean of the variances of the random effects due
to country for each of the MDASI interference items was also
smaller than individual differences, demonstrated by random-
effects estimates less than 1.0 for each of the interference items.
The smallest random-effects variance was observed for the inter-
ference with work item (s2 = 0.14), whereas the relations with
others item (s2 = 0.85) was the most affected. The variances for the
items activity, walking, mood, and enjoyment of life ranged from
0.30 to 0.40.
Effect of Performance Status and Demographics on
To facilitate the comparison of random effects due to country, we
next compared the SDs of the random effects for country (s, which
is on the same scale as the regression coefficients) with the ECOG
PS variable for each symptom. We found that patients with poor
performance status (ECOG PS = 2–4) consistently reported more
severe symptoms, on average, than patients with good perfor-
mance status (ECOG PS = 0–1) and that the difference in the
magnitude of mean effect between good and poor ECOG PS
was much larger than the SD of the random effect for country.
Table 4 shows that, for most symptom items, the estimates of the
random-effect SD associated with country were roughly compa-
rable to the magnitude of the difference in symptom responses for
patients with ECOG PS 1 vs 0.
After adjusting for country effects, we found that women
reported statistically significantly more severe fatigue, sadness, and
distress (all Ps < .001), and disturbed sleep (P = .003). As expected,
patients receiving chemotherapy reported more severe nausea and
vomiting (all Ps < .001).
The results of this study indicate that national and linguistic (coun-
try) variations in patient responses to the MDASI are small relative
Table 2. Most prevalent moderate to severe M. D. Anderson Symptom Inventory (MDASI) symptoms by country*
% (rank)China, % (rank)Japan, % (rank)Russia, % (rank) Korea, % (rank)
Lack of appetite
* An MDASI rating of 5 or greater on the 0–10 scale indicates a moderate to severe symptom.
† Five most severe symptoms for that column.
Table 3. Comparison of mean and variance of symptom items and
random effects variance of country*
severity, mean (SD)
of variance of
Lack of appetite
Shortness of breath
Numbness or tingling
* Pooled M. D. Anderson Symptom Inventory (MDASI) data.
† Symptoms ordered by severity.
‡ Between-subjects variance = 1.
736 Articles | JNCI Vol. 102, Issue 10 | May 19, 2010
to individual patient-related factors. Analysis of MDASI symptom
and interference ratings from cancer patients in five countries—
the United States, China, Japan, Russia, and Korea—revealed that
the variance of the random effects for country was between 20%
and 50% of the intersubject variance. These results give some re-
assurance that symptom data obtained using various language
versions of the MDASI, and possibly patient-reported symptom
data from other measures, can be pooled to analyze multinational
Previous studies have begun to address cross-cultural equiva-
lence in patient-reported health-related quality-of-life measures.
Although progress has been made toward identifying the arenas
in which equivalency should be established, few studies have
been designed to test assertions about cultural applicability. Such
studies have noted that adapting a PRO measure for cross-
cultural use requires a careful accounting for the differential
impact of culture on results (19) and the establishment of con-
ceptual equivalency (7). Confirmation of an adapted measure’s
psychometric validity and reliability is insufficient evidence of its
suitability for use across cultures (20). A few studies of cross-
cultural comparison have examined the dimensional structure of
certain PRO instruments (21,22) but not within the cancer popu-
lation. Other studies conducted in patients with cancer examined
multidimensional scaling for cancer pain (23) and the use of dif-
ferential item functioning for the European Organisation for
Research and Treatment of Cancer 30-item quality-of-life ques-
tionnaire (24) and FACT–Breast (25) but did not examine the
magnitude of effects of language translation and culture/nationality
on how people respond to these measures. Thus, additional in-
vestigation in internationally coordinated projects is needed to
provide sufficient evidence of measurement equivalence for var-
ious language versions of major health-related quality-of-life
measures (26). Findings of cross-cultural equivalency would
support the international application of clinical guidelines for
symptom management because such guidelines are often based
on symptom ratings.
In this study, we compared variations in MDASI scores attrib-
utable to linguistic and cultural variations with inherent intersub-
ject variations in patient responses. In the interpretation of
retrospective data collected from a single nationality, the results
from our analyses suggest that the magnitude of various cultural or
linguistic backgrounds is likely to be only one-fourth to one-half
of the interpatient variation of MDASI symptom reports obtained
from an otherwise homogeneous population.
In future studies that use PROs, this type of probit analysis
method would allow investigators to estimate the impact on symp-
tom reports of patient or clinical variables, such as sex or ECOG
PS, thus making it possible to determine whether the effects of
such variables are sufficiently important to be included in subse-
quent data analyses. For example, in this study, the average differ-
ence between symptom reports of patients with poor performance
status (ECOG PS = 2–4) and patients with good performance
status (ECOG PS = 0–1) was larger than the country effect for
most symptoms. We note that it is common practice to collapse
ECOG status into poor and good categories when analyzing PRO
data; our analyses thus suggest that ignoring the language effects
associated with the administration of the MDASI instrument is
likely to have a smaller effect on study conclusions than collapsing
ECOG categories in this way. We also found small but statistically
significant effects for sex across samples, with women reporting
more severe fatigue, sadness, sleep disturbance, and distress. Again,
these differences were small compared with overall individual
A finding of a statistically significant country effect in cross-
national and cross-cultural studies affecting symptom report data
might derive from several sources. First, a poor translation from
the original linguistic version could compromise the instruments’
comparability (27). Although there is no empirical evidence in
favor of one specific method of translation of a PRO tool, we used
an internally consistent procedure for translating all versions of the
MDASI (28). In addition, in contrast to the more abstract concepts
assessed in most quality-of-life measures, the MDASI assesses only
Table 4. Comparison of Eastern Cooperative Oncology Group performance status (ECOG PS) effect on symptom item reports and SD of
Pooled dataset, SD of
country random effect†
Effect of ECOG PS
1 vs 02 vs 0 3 vs 0 4 vs 0
Lack of appetite
Shortness of breath
Numbness or tingling
* Pooled M. D. Anderson Symptom Inventory data.
† s = square root of posterior mean of random effects of variance.
JNCI | Articles 737
symptoms; it uses single words or simple phrases for its items and
a straightforward 0 to 10 numeric rating scale, making the transla-
tion of MDASI items relatively simple and the establishment of
equivalency less challenging. In this study, we demonstrated that
the more severe symptoms—fatigue, disturbed sleep, distress, and
pain—are less subject to nation and linguistic effects, as evidenced
by the small effect of country on these symptom items.
Symptom management practice is known to vary from country
to country (29), as well as from one treatment site to another
within a country (30). It is thus not unreasonable to expect that
symptoms might be more severe in countries and sites with less
aggressive symptom management. With the current models, we
did not take into account differences in symptom management
practice between countries as a fixed-effect factor. However, the
consistency in this study in the most severe symptoms reported by
patients, regardless of the characteristics from each sample, as well
as the consistency in patients with poor ECOG PS reporting more
severe symptoms, indicates that the MDASI functioned similarly
across language versions in characterizing symptom burden. In
addition, the very small effect of country on pain ratings from this
study also supports our procedures, given that one would expect
much greater cross-national variation in pain control (ie, variations
in practice of prescribing opioids, which are the World Health
Organization’s recommended standard for management of severe
cancer pain) (31) than in fatigue management (because of lack of
widely used therapeutic methods to control fatigue).
The study had several limitations. First, the effect of country
was confounded with the effect of the individual sample site
because only one to three treatment centers were sampled in each
country. This could prevent a full examination of patient cultural
differences within the same language from country to country.
Second, although we expect that language differences in symptom
reporting will be even smaller with more homogeneous samples,
this expectation needs to be tested empirically. The current
analysis used pooled data from heterogeneous samples and did
identify one MDASI interference item—relations with others—
that was affected by country. Additional research is needed, and
caution is warranted when interpreting the meaning of “relations
with others” in international MDASI data. Third, we were not able
to use cancer stage as a covariate in the modeling because data
from one country for this variable were missing. However, the
similarity in the percentage of patients with metastasis indicates a
comparable disease status across all samples.
In conclusion, this analysis suggests that once psychometrically
sound translations of the MDASI have been established, various
language versions can be used to gather symptom severity and in-
terference ratings that can be interpreted in a similar way across
patient nationalities. The generalizability is meaningful for inter-
preting the results across various cultural and language groups and
provides greater utility in symptom assessment for oncology prac-
tice, clinical trials, and clinical research—not only among the di-
versity of patients in the United States but also for patients with
cancer in other parts of the world.
1. Cleeland CS. Symptom burden: multiple symptoms and their impact as
patient-reported outcomes. J Natl Cancer Inst Monogr. 2007;37:16–21.
2. US Food and Drug Administration, Center for Drug Evaluation and
Research, Center for Biologics Evaluation and Research, Center for Devices
and Radiological Health. Guidance for industry (draft). Patient-reported out-
come measures: use in medical product development to support labeling claims. 2009.
ormation/Guidances/UCM071975.pdf. Accessed March 15, 2010.
3. Benedetti C, Brock C, Cleeland C, et al. NCCN practice guidelines for
cancer pain. Oncology (Huntington). 2000;14(11A):135–150.
4. Berger AM, Abernethy AP, Atkinson A, et al. NCCN Clinical Practice
Guidelines in Oncology: cancer-related fatigue. 2008. http://www.nccn.org
/professionals/physician_gls/PDF/fatigue.pdf. Accessed December 10, 2008.
5. Swarm R, Anghelescu DL, Benedetti C, et al. Adult cancer pain. J Natl
Compr Canc Netw. 2007;5(8):726–751.
6. Eaton LH, Tipton JM. Putting Evidence Into Practice: Improving Oncology
Patient Outcomes. 1st ed. Pittsburgh, PA: Oncology Nursing Society; 2009.
7. Bowden A, Fox-Rushby JA. A systematic and critical review of the process
of translation and adaptation of generic health-related quality of life
measures in Africa, Asia, Eastern Europe, the Middle East, South America.
Soc Sci Med. 2003;57(7):1289–1306.
8. Cleeland CS, Mendoza TR, Wang XS, et al. Assessing symptom distress
in cancer patients: the M.D. Anderson Symptom Inventory. Cancer.
9. Kirkova J, Davis MP, Walsh D, et al. Cancer symptom assessment instru-
ments: a systematic review. J Clin Oncol. 2006;24(9):1459–1473.
10. Wang XS, Wang Y, Guo H, Mendoza TR, Hao XS, Cleeland CS.
Chinese version of the M.D. Anderson Symptom Inventory: validation
and application of symptom measurement in cancer patients. Cancer.
11. Okuyama T, Wang XS, Akechi T, et al. Japanese version of the M.D.
Anderson Symptom Inventory: a validation study. J Pain Symptom Manage.
12. Ivanova MO, Ionova TI, Kalyadina SA, et al. Cancer-related symptom
assessment in Russia: validation and utility of the Russian M.D. Anderson
Symptom Inventory. J Pain Symptom Manage. 2005;30(5):443–453.
13. Yun YH, Mendoza TR, Kang IO, et al. Validation study of the Korean
version of the M.D. Anderson Symptom Inventory. J Pain Symptom
14. Wang XS, Mendoza TR, Gao SZ, 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(2–3):407–416.
15. Cronbach LJ. Coefficient alpha and the internal structure of tests.
16. Johnson VE, Albert J. Ordinal Data Modeling. New York, NY: Springer-
17. Oken MM, Creech RH, Tormey DC, et al. Toxicity and response criteria of the
Eastern Cooperative Oncology Group. Am J Clin Oncol. 1982;5(6):649–655.
18. Serlin RC, Mendoza TR, Nakamura Y, Edwards KR, Cleeland CS. When
is cancer pain mild, moderate or severe? Grading pain severity by its inter-
ference with function. Pain. 1995;61(2):277–284.
19. de Haes JC, Olschewski M. Quality of life assessment in a cross-cultural
context: use of the Rotterdam Symptom Checklist in a multinational ran-
domised trial comparing CMF and Zoladex (Goserlin) treatment in early
breast cancer. Ann Oncol. 1998;9(7):745–750.
20. Wagner AK, Gandek B, Aaronson NK, et al. Cross-cultural comparisons
of the content of SF-36 translations across 10 countries: results from the
IQOLA Project. International Quality of Life Assessment. J Clin Epidemiol.
21. Allik J. Personality dimensions across cultures. J Pers Disord. 2005;
22. Kim M, Han HR, Phillips L. Metric equivalence assessment in cross-cul-
tural research: using an example of the Center for Epidemiological
Studies–Depression Scale. J Nurs Meas. 2003;11(1):5–18.
23. Cleeland CS, Nakamura Y, Mendoza TR, Edwards KR, Douglas J, Serlin RC.
Dimensions of the impact of cancer pain in a four country sample: new
information from multidimensional scaling. Pain. 1996;67(2–3):267–273.
24. Scott NW, Fayers PM, Bottomley A, et al. Comparing translations of the
EORTC QLQ-C30 using differential item functioning analyses. Qual Life
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25. Hahn EA, Holzner B, Kemmler G, Sperner-Unterweger B, Hudgens SA,
Cella D. Cross-cultural evaluation of health status using item response
theory: FACT-B comparisons between Austrian and U.S. patients with
breast cancer. Eval Health Prof. 2005;28(2):233–259.
26. Anderson RT, Aaronson NK, Bullinger M, McBee WL. A review of the
progress towards developing health-related quality-of-life instruments for
international clinical studies and outcomes research. Pharmacoeconomics.
27. Acquadro C, Conway K, Hareendran A, Aaronson N. Literature review of
methods to translate health-related quality of life questionnaires for use in
multinational clinical trials. Value Health. 2008;11(3):509–521.
28. Guillemin F, Bombardier C, Beaton D. Cross-cultural adaptation of
health-related quality of life measures: literature review and proposed
guidelines. J Clin Epidemiol. 1993;46(12):1417–1432.
29. World Health Organization. Cancer pain relief: with a guide to opioid
availability. 1994. http://whqlibdoc.who.int/publications/9241544821
.pdf. Accessed July 14, 2009.
30. Cleeland CS, Gonin R, Hatfield AK, et al. Pain and its treatment in
outpatients with metastatic cancer. N Engl J Med. 1994;330(9):592–596.
31. Laugsand EA, Kaasa S, de CF, Hanks G, Klepstad P. Intensity and treat-
ment of symptoms in 3,030 palliative care patients: a cross-sectional sur-
vey of the EAPC Research Network. J Opioid Manag. 2009;5(1):11–21.
National Cancer Institute of the National Institutes of Health (R01 CA026582
to C.S.C.); United States Cancer Pain Relief Committee; Hawn Foundation,
The content is solely the responsibility of the authors and does not neces-
sarily represent the official views of the National Cancer Institute or the
National Institutes of Health. The study sponsor had no role in the design of
the study; the collection, analysis, and interpretation of the data; the writing
of the manuscript; or the decision to submit the manuscript for publication.
The authors acknowledge the editorial assistance of Jeanie F. Woodruff, ELS.
Affiliations of authors: Department of Symptom Research (XSW, CSC,
TRM) and Department of Biostatistics (VEJ), The University of Texas M. D.
Anderson Cancer Center, Houston, Texas; Department of Family Medicine,
National Cancer Center, Goyang, Gyeonggi, Korea (YHY); Tianjin Cancer
Hospital, Research and Educational Department, Tianjin Medical University
Cancer Institute and Hospital, Tianjin, China (YW); Department of Psychiatry,
Nagoya City University Graduate School of Medical Sciences, Nagoya,