Rasch analysis of the Chinese version of the clinically useful depression outcome scale in patients with major depressive disorder
BMC Psychology volume 11, Article number: 218 (2023)
To examine the psychometric properties of the Chinese version of the Clinically Useful Depression Outcome Scale (CUDOS) in the Chinese patients with major depressive disorder (MDD) using Rasch analysis.
The sample consisted of 283 patients with MDD (69% females). The Rasch model was applied to examine the overall fit of the Chinese version of CUDOS and the fit of the 18 items. Dimensionality, item-model fit, differential item functioning (DIF), reliability, ordering of response category and targeting were tested to examine the psychometric properties of the Chinese version of CUDOS.
Rasch analysis demonstrated the unidimensionality of the Chinese version of CUDOS. Of the 18 items, three items (item 4, item 5, item 6) showed misfit in the model. After merging item 4 into item 3 and item 6 into item 5, the overall model fit improved. The person separation index (PSI) was 3.0 and the person reliability coefficient was 0.90. No evidence of significant DIF was found when associated with gender and age. No disordered category and threshold of the rating response were observed, which meant the response category setting was reasonable. The mean ability of person was − 0.53.
The results suggested that the Chinese version of CUDOS has acceptable psychometric properties. In order to improve the quality and applicability of the Chinese version of CUDOS, the merging of item 4 into item 3 and item 6 into item 5 are suggested.
Major depressive disorder (MDD) is a common mental disorder with high rates of morbidity, recurrence, disability and suicide, which has become a significant public health problem of worldwide concern [1,2,3,4]. Approximately 322 million people suffer from depression around the world, which is 4.4% of the global population . A cross-sectional epidemiological study showed that the lifetime prevalence of MDD in mainland China was about 3.4% . Moreover, World Health Organization (WHO) predicted that MDD would become a leading cause of the global burden of disease by 2030 . Remission is recognized as the optimal outcome of treatment for depression. However, low rates of remission and high rates of relapse commonly appear in clinical consequences, which contribute to impaired social function and reduced quality of life [8, 9]. In clinical practice, measurement-based care (MBC) has proved to be of great benefit in the treatment for patients with MDD. Clinicians can be able to adjust treatment strategies by referring to the results of measurements, which may promote treatment outcomes . Therefore, a reliable and valid instrument to measure clinical outcomes for patients with MDD is required.
Clinically Useful Depression Outcome Scale (CUDOS) is a brief self-administered depression questionnaire developed by Zimmerman that contains 18 items. It is easy to use that takes around less than 3 min to complete and less than 15 s to score. The CUDOS was designed according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria for MDD, proved to be clinically useful and sensitive to the changes of depressive symptoms . In addition, the CUDOS evaluates not only severity of depressive symptoms but also psychological impairment and life quality, which provides clinicians with more useful information of treatment outcomes . The CUDOS is also user-friendly, it has less burden of scale completion to patients so they are willing to complete it regularly . The original English version of CUDOS has been translated into multiple languages and all these translated versions have consistently demonstrated good reliability and validity [14,15,16,17].
In our previous study, we examined the psychometric properties of the Chinese version of CUDOS in patients with MDD using traditional Classical Test Theory (CTT). Depressive symptoms were assessed in 190 patients with MDD using the Chinese version of the CUDOS, 17-item Hamilton Depression Rating Scale (HRSD) and the modified Overall Clinical Impression-Severity Scale 15 (iCGI-S). Reliability, validity tests, and receiver operating characteristic curves were performed. The result showed that the Chinese version of CUDOS was of great value as a brief and reliable tool to assess depressive symptoms and clinical outcome . However, the result reflected only the overall performance of the Chinese version of CUDOS, because of the limitations of CTT, it could not provide detailed information of individual item performance. Moreover, the test dependence and sample dependence of CTT, may also contributed to non-objectivity of the measurement results [18, 19]. The Item Response Theory (IRT) was developed to compensate for the limitations of CTT. Rasch model is one of the IRT models that emphasizes one-parameter and unidimensional research paradigm, which is relatively simple comparing to other IRT models [20,21,22]. In recent years, the application of Rasch analysis in health outcome measures has become popular [23,24,25]. It has advantages comparing to CTT, including displaying test and sample independence, providing linear transformation of the ordinal raw score and diagnostic details on how the scale can be improved by exploring the performance of individual items [23, 26].
The purpose of the current study was to evaluate the psychometric properties of the Chinese version of CUDOS by using Rasch analysis. Dimensionality, item-model fit, differential item functioning (DIF), reliability, response category ordering, and targeting were assessed in patients with MDD to diagnose potential measurement problems and to make recommendations for improving the quality and applicability of the Chinese version of the CUDOS in patients with MDD.
The participants consisted of 283 patients with MDD recruited from Guangdong Mental Health Center in China between October 2018 and August 2021. Patients were included if the following criteria were met: (1) diagnosis of MDD based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), and it was diagnosed by two psychiatrists with attending or above professional titles;(2) the aged from 18 to 65 years old;(3) all patients signed an informed consent form. Patients were excluded if the following criteria were met: (1) patients suffering from other mental disorders (such as bipolar disorder, etc.);(2) patients suffering from severe physical illness;(3) patients with a history of substance abuse (e.g.alcohol and drugs) within the past year;(4) women in pregnancy or breastfeeding.
First, a psychiatrist with an attending title or higher confirmed the participants’ diagnosis according to the DSM-5. Followed by an interview with MDD patients by a trained researcher, who presented the intent and content of the study to patients with MDD. Patients with MDD voluntarily participated and signed an informed consent form. Patients with MDD who met the inclusion and exclusion criteria were included in this study. Subsequently, general demographic and clinical characteristics (e.g., gender, age, marital status, family history, duration of depression and years of education, etc.) were collected from patients with MDD. Finally, patients with MDD completed a self-report scale assessment in a quiet room.
The CUDOS was a self-report questionnaire for assessing the depressive symptoms and identifying remission status according to the DSM-IV [13, 27]. The CUDOS consisted of 18 items evaluating all of the DSM-IV criteria for MDD as well as psychosocial impairment and the impact of depressive symptoms on life quality. Each item was assessed by utilizing a 5-point Likert-type scale. Patients chose a number according to their condition during the past week (including today): 0 = not at all true/0 days; 1 = rarely true/1–2 days; 2 = sometimes true/3–4 days; 3 = usually true/5–6 days; 4 = almost always true/everyday, with total scores ranging from 0 to 72 points . In the current study, the Chinese version of the CUDOS was used to assess depressive symptoms. Good reliability and validity of the scale have been demonstrated through CTT method .
The collected data were analyzed by descriptive statistics using SPSS version 26.0 (IBM Corporation, Chicago, IL). WINSTEPS 4.8.2 software was used to conduct Rasch analysis. The analysis included dimensionality, item-model fit, DIF, reliability, ordering of response categories and targeting. The sample size was based on definitive or high stakes with best to poor targeting sample size of exceeding 250 samples at 99% confidence .
In Rasch analysis, dimensionality was analyzed by principal component analysis (PCA). The eigenvalue of the first contrast was suggested to be between 1.4 and 2.1 [29, 30]. In addition,a proportion of variance that could be explained by Rasch model exceeded 50% indicated the construct of the scale was unidimensional . Pearson correlations > 0.57 was suggested to be acceptable . While Rasch model divided the items into several clusters, the high disattenuated correlation (r > 0.3) between 2 clusters indicated that these clusters might perform a same dimension.
Wright and Linacre(1994) suggested the values of information-weighted mean-square fit statistic (infit MnSq) and outlier-sensitive mean-square fit statistics (outfit MnSq) were closer to 1, indicating that data fitted Rasch model . For clinical purposes, the values of infit and outfit MnSq of individual item should be between 0.5 and 2.0, or else it would be considered as a misfitting item . Item-model fit could also be evaluated graphically by the Item Characteristic Curve (ICC), which indicated to fit well if plots fall on the expected curve .
Differential item functioning (DIF)
DIF analysis was conducted to identify the systematic and random bias of the measurement . DIF was assessed by comparing participants in different groups matched for trait levels. In this study, patients with MDD were categorized based on gender (female/male) and age (split at median:18–26 y/27–65 y), respectively. The DIF contrast > 0.64 was considered to be notable .
Item reliability and person reliability indices should fall between 0 and 1 . The item separation index (ISI) and person separation index (PSI) must exceed 2.0 to ensure the separation reliability coefficient to be above 0.8. In addition, person reliability coefficient was associated with Cronbach’s ɑ . The Cronbach’s ɑ exceeding 0.8 was considered to be satisfactory .
Ordering of response category
To test the ordering of response categories we examined fit values and average measures of the categories and thresholds. The category probability curves (CPC) provided visualization of response category function . The infit MnSq and outfit MnSq statistics should be between 0.5 and 1.7. Moreover, average category measures should monotonically increase with categories . The thresholds between adjacent categories should be between 1.4 and 5 logits, and monotonically increased with categories .
Item-person map (Wright map) displayed the item location and person location on the same logit scale . Differences of greater than 1.0 logits between person mean measures and item mean measures were considered to be notably mistargeting [33, 37]. Items with logit values below 0 were relatively easier, and items with logit values exceed 0 were relatively difficult .
A total of 298 patients with MDD were investigated. 15 patients were excluded for lacking of sociodemographic and clinical characteristics. Finally, 283 patients were enrolled in this study, of which 31.1% were male and 68.9% were female. The age range from 18 to 61 with a mean age of 29.02 years and a standard deviation (SD) of 0.60 years. Besides, 68 (24.0%) patients had family history of mental disorders, and 171 (60.4%) were first-episode patients. Table 1 showed the demographic information and the clinical characteristics.
The eigenvalue of the first contrast was 2.6. The proportion of raw variance data that can be explained by Rasch model was 56.7%. In addition, the rasch analysis divided the data into 3 clusters (Fig. 1). The person correlations between 3 clusters of items ranged from 0.62 to 0.79, and the disattenuated correlations between 3 clusters of items ranged from 0.89 to 1.00 (Table 2).
Items above the zero horizontal line indicate positive loading and items below indicate negative loading.
Overall infit and outfit statistics of items and person were close to 1 (Table 2). The great majority of individual items had infit and outfit MnSq between 0.5 and 1.7. But the outfit statistics of item 4 (My appetite was much greater than usual), item 5 (I had difficulty sleeping) and item 6 (I was sleeping too much) were outside this range. The outfit statistics values of item 4, item 5 and item 6 were 3.82, 1.85 and 2.57, respectively, and the infit statistics values of item 4, item 5 and item 6 were 2.09, 1.75 and 2.30, respectively (Table 3). The overall fit statistic was presented in Table 2 and the individual items fit statistic was presented in Table 3. The ICC showed that most of plots fell on the expected curve and all of these points were within the 95% confidence interval (Fig. 2). After merging item 4 into item 3 and item 6 into item 5, the overall model fit improved (Table 4) and the infit and outfit statistics for all individual items of the scale were with in the acceptable range (Table 5).
The most of plots fell on the expected curve and all of these plots were within the 95% confidence interval.
Differential item functioning (DIF)
DIF analysis was performed to detect whether DIF existed according to gender (female/male) and age (18–26 y/27–65 y). Results showed that there was no DIF contrast statistics greater than 0.64 (Table 3).
The ISI and the PSI statistics were 7.03 and 3.00 respectively (Table 2). Besides, the item reliability coefficient was 0.98 and the person reliability coefficient was 0.90 (Table 2). The reliability of all items were at an acceptable level.
Ordering of response category
The summary of category structure statistics presented in Table 6. All category infit MnSq and outfit MnSq statistics were in the range of 0.5 to 1.7. And the average measure increased monotonically from − 2.17 to 2.14. The threshold increased monotonically as well with category from − 0.73 to 0.67. However, the thresholds differences between adjacent categories were less than 1.4. The CPC showed that all thresholds were ordered (Fig. 3).
Each curve represented the probability of endorsing a response option. The red, blue, pink,black and green curves on the graph represent the 0, 1, 2, 3 and 4 rating categories respectively.
All of the individual item measures were between − 0.59 and 1.28 logits, with item 4 (My appetite was much greater than usual) was the hardest item and item 18 (How would you rate your overall quality of life during the past week?) was the easiest item (Table 3). Furthermore, the person mean measure was − 0.53 logits, and the item mean measure was 0 (Table 2). Besides, the item-person map (Wright map) compared the correspondence between the mean person locations and the mean item locations (Fig. 4), and most of items and person locations were near 0.
The Chinese version of CUDOS had been validated in Chinese patients with MDD by CTT methods, which display good content validity, calibration validity, and discriminant validity . Yet there was necessary to confirm the psychometric properties of the Chinese version of CUDOS in detail before it could be widely adopted. Rasch model derived from IRT methods that could provide more details of the measure and compensate the defects of CTT methods . In this study, the Rasch analysis had identified some strengths and limitations of the Chinese version of CUDOS that were not previously observed when using CTT methods.
Firstly, the dimensionality of the Chinese version of CUDOS was tested. The eigenvalue of the first contrast was slightly higher than the criterion, which might indicate a multi-dimensionality. However, the proportion of raw variance data, high disattenuated correlations and person correlations suggested that the Chinese version of CUDOS was a unidimensional contruct scale, which was consistent with those previous studies by CTT methods [11, 14].
As to the fit analysis, the Chinese version of CUDOS demonstrated that the majority of items adequately fitted the model. Three items showed poor outfit statistics (misfitting), the item 4 (My appetite was much greater than usual), item 5 (I had difficulty sleeping) and item 6 (I was sleeping too much).Misfitting items were suggested to be deleted or modified theoretically. Given that difficulty sleeping was a common and important symptom of MDD which should be retained, it was necessary to modify or adjust the language description of item 5. Though item 4 and item 6 were not typical symptoms of MDD, deleting them directly might result in a lack of information for assessment in clinical practice. Since item 3 and item 4 fell in the same category, so did item 5 and item 6. Merging item 4 into item 3 and item 6 into item 5 were considered. The results showed that the overall model fit improved.
To investigate the possibility of item bias, DIF analysis was conducted to determine if items exhibited gender- and age-based DIF. The items did not show evidence of DIF across gender or age in the sample of patients with MDD, indicating that prominent item bias was not found in the Chinese version of CUDOS.
PSI in Rasch analysis associates Cronbach’s ɑ . The original English version of CUDOS demonstrated good reliability with a Cronbach’s ɑ of 0.90 . The obtained results indicated that the Chinese version of CUDOS has good reliability, indicating that the scale was a reliable tool for patients with MDD.
As a proper rating scale, each of the items, respondents with high levels of the attribute being measured are supposed to endorse high scoring responses. The results indicated that the ordering of response categories were reasonable in most aspects, and disordered response categories or thresholds was not found. However, thresholds between adjacent categories were too close to meet the criteria (< 1.4 logits), which indicated that the adjacent response categories were not distinguishing enough. Probably it was because the sample was homogeneous,meanwhile, splitting a week time into five response categories might lead to a narrow margin between adjacent options.
The Rasch analysis transforms raw scores to be interval and then compare person ability and item difficulty in a same logit scale [38, 39]. In clinical practice, the measurement used are appropriately targeted at the population being assessed . The Chinese version of CUDOS total scale person-item map showed that items were evenly distributed around 0. The difference between person mean and item mean measure were less than 1.0 logits. The results indicated the severity of depressive symptoms in patients with MDD could be accurately captured by the items in the Chinese version of CUDOS.
This study had several limitations. Firstly, all participants were recruited at the Guangdong Mental Health Center in China. Further multi-center studies should be conducted in the future to explore the applicability of the Chinese version of CUDOS in patients with MDD. Secondly, the results might be influenced by selection bias, as few middle-aged and elderly subjects were included. Therefore, the data might not represent an accurate cross-sectional study. Moreover, structured interviews were not adopted in this study, which somewhat weakened the strength of homogeneity of the sample. The applicability of the Chinese version of CUDOS in healthy population needs to be further explored. Finally, only 1-parameter logistic model was considered in this study, and 2-parameter logistic model and 3-parameter logistic model can be further used for comparison in the future and try to find the difference of the results.
In summary, the Chinese version of CUDOS is a reliable tool to evaluate depression symptoms for patients with MDD. Rasch analysis of the Chinese version of CUDOS largely confirmed the unidimensionality of the instrument. There was no notable differential item functioning (DIF) across either gender or age. No disordered response category was found. And the scale had a well-targeted measure. In order to improve the quality and applicability of the scale, it is suggested that item 4 be merged into item 3 and item 6 into item 5.
Due to ethical restrictions, the present study data were not publicly available to ensure that research participants privacy is not compromised. Data for this study are available from the corresponding author, Dr. Xu.
Peng R, Wang Y, Huang Y, Liu Z, Xu X, Ma Y, Wang L, Zhang M, Yan Y, Wang B, et al. The association of depressive symptoms with disability among adults in China. J Affect Disord. 2022;296(1):189–97.
Dong M, Wang SB, Li Y, Xu DD, Ungvari GS, Ng CH, Chow I, Xiang YT. Prevalence of suicidal behaviors in patients with major depressive disorder in China: a comprehensive meta-analysis. J Affect Disord. 2018;225:32–9.
Cai H, Jin Y, Liu S, Zhang Q, Zhang L, Cheung T, Balbuena L, Xiang YT. Prevalence of suicidal ideation and planning in patients with major depressive disorder: a meta-analysis of observation studies. J Affect Disord. 2021;293:148–58.
Xiang YT, Cai H, Sun HL, Su Z, Cheung T, Ng CH. Prevalence of mental disorders in China. LANCET PSYCHIAT. 2022;9(1):13–4.
Friedrich MJ. Depression is the leading cause of disability around the World. JAMA. 2017;317(15):1517.
Huang Y, Wang Y, Wang H, Liu Z, Yu X, Yan J, Yu Y, Kou C, Xu X, Lu J, et al. Prevalence of mental disorders in China: a cross-sectional epidemiological study. LANCET PSYCHIAT. 2019;6(3):211–24.
Celie JE, Loeys T, Desmet M, Verhaeghe P. The Depression Conundrum and the advantages of uncertainty. FRONT PSYCHOL. 2017;8:939.
Mojtabai R, Amin-Esmaeili M, Spivak S, Olfson M. Remission and treatment augmentation of Depression in the United States. J Clin Psychiatry. 2021;82(6):21m–13988m.
Keller MB. Past, present, and future directions for defining optimal treatment outcome in depression: remission and beyond. JAMA. 2003;289(23):3152–60.
Harding KJ, Rush AJ, Arbuckle M, Trivedi MH, Pincus HA. Measurement-based care in psychiatric practice: a policy framework for implementation. J Clin Psychiatry. 2011;72(8):1136–43.
Zimmerman M, Chelminski I, McGlinchey JB, Posternak MA. A clinically useful depression outcome scale. COMPR PSYCHIAT. 2008;49(2):131–40.
Zimmerman M, Ruggero CJ, Chelminski I, Young D, Posternak MA, Friedman M, Boerescu D, Attiullah N. Developing brief scales for use in clinical practice: the reliability and validity of single-item self-report measures of depression symptom severity, psychosocial impairment due to depression, and quality of life. J Clin Psychiatry. 2006;67(10):1536–41.
Zimmerman M, McGlinchey JB. Depressed patients’ acceptability of the use of self-administered scales to measure outcome in clinical practice. ANN CLIN PSYCHIATRY. 2008;20(3):125–9.
Ma HY, Wang XM, Huang XJ, Yang CJ, Sheng DF, Yang JJ, Xu MZ. Psychometric Properties of the Chinese Version of the clinically useful Depression Outcome Scale for patients with major depressive disorder. Neuropsychiatr Dis Treat. 2021;17:2387–95.
Jeon SW, Han C, Ko YH, Yoon SY, Pae CU, Choi J, Park YC, Kim JW, Yoon HK, Ko SD, et al. Measurement-based treatment of residual symptoms using clinically useful Depression Outcome Scale: korean validation study. Clin Psychopharmacol Neurosci. 2017;15(1):28–34.
Trujols J, Feliu-Soler A, de Diego-Adeliño J, Portella MJ, Cebrià Q, Soler J, Puigdemont D, Alvarez E, Perez V. A psychometric analysis of the clinically useful Depression Outcome Scale (CUDOS) in spanish patients. J Affect Disord. 2013;151(3):920–3.
Mauna GY, Jami H. Urdu translation and validation of clinically useful depression outcome scale. J PAK MED ASSOC. 2021;71(11):2524–30.
Wright B, Linacre J. Reasonable mean-square fit values. Rasch Meas Trans. 1994;8:370.
Ing LGUQ. Applications of Rasch model in the Liket scale. J Guizhou Normal Univ (Natural Sciences). 2012;30(1):13–6.
Tennant A, McKenna SP, Hagell P. Application of Rasch analysis in the development and application of quality of life instruments. VALUE HEALTH. 2004;7(Suppl 1):22–S26.
van Alphen A, Halfens R, Hasman A, Imbos T. Likert or Rasch? Nothing is more applicable than good theory. J ADV NURS. 1994;20(1):196–201.
Conrad KJ, Smith EJ. International conference on objective measurement: applications of Rasch analysis in health care. MED CARE. 2004;42(1 Suppl):I1–I6.
Petrillo J, Cano SJ, McLeod LD, Coon CD. Using classical test theory, Item Response Theory, and Rasch Measurement Theory to evaluate patient-reported outcome measures: a comparison of worked examples. VALUE HEALTH. 2015;18(1):25–34.
Vindbjerg E, Mortensen EL, Makransky G, Nielsen T, Carlsson J. A rasch-based validity study of the HSCL-25. J Affect Disorders Rep. 2021;4:100096.
Jiraniramai S, Wongpakaran T, Angkurawaranon C, Jiraporncharoen W, Wongpakaran N. Construct Validity and Differential Item Functioning of the PHQ-9 among Health Care Workers: Rasch Analysis Approach. Neuropsychiatr Dis Treat. 2021;17:1035–45.
Chachamovich E, Fleck MP, Trentini CM, Laidlaw K, Power MJ. Development and validation of the brazilian version of the Attitudes to Aging Questionnaire (AAQ): an example of merging classical psychometric theory and the Rasch measurement model. Health Qual Life Outcomes. 2008;6:5.
Zimmerman M, Posternak MA, Chelminski I. Using a self-report depression scale to identify remission in depressed outpatients. Am J Psychiatry. 2004;161(10):1911–3.
Linacre JM. Sample size and item Calibration Stability. Rasch Meas Trans. 1994;7:328.
Linacre JM. TA: more about critical eigenvalue sizes (variances) in standardized-residual principal components analysis (PCA). Rasch Meas Trans. 2009;23(3):1228.
Raîche G. Critical eigenvalue sizes in standardized residual principal components analysis. Rasch Meas Trans 2005, 19.
Linacre J. Optimizing rating scale category effectiveness. J Appl Meas. 2002;3(1):85–106.
Khadka J, Fenwick EK, Lamoureux EL, Pesudovs K. Item Banking enables stand-alone measurement of driving ability. Optom Vis Sci. 2016;93(12):1502–12.
Linacre J. A User’’s Guide to Winsteps: Rasch-Model Computer Program. 2002.
Andrich D, Hagquist C. Real and Artificial Differential Item Functioning in Polytomous items. EDUC PSYCHOL MEAS. 2015;75(2):185–207.
Boone W, Staver J, Yale M. Rasch Analysis in the Human Sciences; 2014.
Tavakol M, Dennick R. Making sense of Cronbach’s alpha. Int J Med Educ. 2011;2:53–5.
Kandel H, Pesudovs K, Ferdi A, Mills R, Chen JY, Watson A, Poon A, Downie LE, Watson SL. Psychometric Properties of the Keratoconus Outcomes Research Questionnaire: a Save Sight Keratoconus Registry Study. Cornea. 2020;39(3):303–10.
Andrich D. An extension of the Rasch model for ratings providing both location and dispersion parameters. Psychometrika. 1982;47:105–13.
Zi Y. Objective measurement in Psychological Science: an overview of Rasch Model. Adv Psychol Sci. 2010;18(8):1298–305.
Pallant JF, Tennant A. An introduction to the Rasch measurement model: an example using the hospital anxiety and Depression Scale (HADS). BRIT J CLIN PSYCHOL. 2011;46(Pt 1):1–18.
This study was completed in Guangdong Mental Health Center, Guangdong Provincial People’s Hospital and the authors appreciate all the colleagues for their support and assistance. Thanks to all the participants for their support. Finally, We really appreciate Professor Fan Chang-He, Professor Hou Cai-Lan and Professor Jiang Mei-Jun for doing translation efforts and providing quality checks on translations.
The current study received funding from the Guangdong Science and Technology Project (project NO: 2017A020215095).
The authors declare no conflict of interest.
Ethics approval and consent to participate
This study protocol was approved by the Clinical Research Ethics Committee of Guangdong Provincial People’s Hospital (No. GDREC2018156H (R1)). This study was performed in accordance with the principles expressed in the Declaration of Helsinki. All participants have provided written informed consent to participate in this study.
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Zhong, J., Ma, HY., Wang, XM. et al. Rasch analysis of the Chinese version of the clinically useful depression outcome scale in patients with major depressive disorder. BMC Psychol 11, 218 (2023). https://doi.org/10.1186/s40359-023-01255-7