PurposeThe classification of patients into distinct categories of quality of life (QoL) levels may be useful for clinicians to interpret QoL scores from multidimensional questionnaires. The aim of this study had been to define clusters of QoL levels from a specific multidimensional questionnaire (SQoL18) for patients with schizophrenia by using a new method of interpretable clustering and to test its validity regarding socio-demographic, clinical, and QoL information.MethodsIn this multicentre cross-sectional study, patients with schizophrenia have been classified using a hierarchical top-down method called clustering using unsupervised binary trees (CUBT). A three-group structure has been employed to define QoL levels as “high”, “moderate”, or “low”. Socio-demographic, clinical, and QoL data have been compared between the three clusters to ensure their clinical relevance.ResultsA total of 514 patients have been analysed: 78 are classified as “low”, 265 as “moderate”, and 171 as “high”. The clustering shows satisfactory statistical properties, including reproducibility (using bootstrap analysis) and discriminancy (using factor analysis). The three clusters consistently differentiate patients. As expected, individuals in the “high” QoL level cluster report the lowest scores on the Positive and Negative Syndrome Scale (p = 0.01) and the Calgary Depression Scale (p < 0.01), and the highest scores on the Global Assessment of Functioning (p < 0.03), the SF36 (p < 0.01), the EuroQol (p < 0.01), and the Quality of Life Inventory (p < 0.01).ConclusionGiven the ease with which this method can be applied, classification using CUBT may be useful for facilitating the interpretation of QoL scores in clinical practice.
OBJECTIVE: Quality of life (QoL) measurements are considered important outcome measures both for research on multiple sclerosis (MS) and in clinical practice. Computerized adaptive testing (CAT) can improve the precision of measurements made using QoL instruments while reducing the burden of testing on patients. Moreover, a cross-cultural approach is also necessary to guarantee the wide applicability of CAT. The aim of this preliminary study was to develop a calibrated item bank that is available in multiple languages and measures QoL related to mental health by combining one generic (SF-36) and one disease-specific questionnaire (MusiQoL).
METHODS: Patients with MS were enrolled in this international, multicenter, cross-sectional study. The psychometric properties of the item bank were based on classical test and item response theories and approaches, including the evaluation of unidimensionality, item response theory model fitting, and analyses of differential item functioning (DIF). Convergent and discriminant validities of the item bank were examined according to socio-demographic, clinical, and QoL features.
RESULTS: A total of 1992 patients with MS and from 15 countries were enrolled in this study to calibrate the 22-item bank developed in this study. The strict monotonicity of the Cronbach's alpha curve, the high eigenvalue ratio estimator (5.50), and the adequate CFA model fit (RMSEA = 0.07 and CFI = 0.95) indicated that a strong assumption of unidimensionality was warranted. The infit mean square statistic ranged from 0.76 to 1.27, indicating a satisfactory item fit. DIF analyses revealed no item biases across geographical areas, confirming the cross-cultural equivalence of the item bank. External validity testing revealed that the item bank scores correlated significantly with QoL scores but also showed discriminant validity for socio-demographic and clinical characteristics.
CONCLUSION: This work demonstrated satisfactory psychometric characteristics for a QoL item bank for MS in multiple languages. This work may offer a common measure for the assessment of QoL in different cultural contexts and for international studies conducted on MS.
Statistical modeling conference on the quality of life measurements of the French National Platform of Quality of Life and Cancer Faculty of Science in Luminy, Marseille, France, 12-13 September 2013 The French National Platform of Quality of Life and Cancer and the statistical team of the Mathematical Institute of Luminy undertook a successful first conference addressing the statistical challenges of measuring the quality of life in the field of oncology. More than 15 presentations were made over a 2-day period by the Faculty of Sciences in Luminy. The conference managed to assemble participants from different disciplines, such as mathematics and statistics, public health, epidemiology and psychology, to debate the key statistical and methodological issues of quality of life measurement and analysis. Three main topics were covered in this conference: the treatment of missing data, the development of item banking and computerised adaptive testing and the detection/understanding of response shift.