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Objective To evaluate the psychometric properties of the seven theoretical models proposed in the literature for European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30), when applied to a sample of Brazilian cancer patients. Methods Content and construct validity (factorial, convergent, discriminant) were estimated. Confirmatory factor analysis was performed. Convergent validity was analyzed using the average variance extracted. Discriminant validity was analyzed using correlational analysis. Internal consistency and composite reliability were used to assess the reliability of instrument. Results A total of 1,020 cancer patients participated. The mean age was 53.3±13.0 years, and 62% were female. All models showed adequate factorial validity for the study sample. Convergent and discriminant validities and the reliability were compromised in all of the models for all of the single items referring to symptoms, as well as for the "physical function" and "cognitive function" factors. Conclusion All theoretical models assessed in this study presented adequate factorial validity when applied to Brazilian cancer patients. The choice of the best model for use in research and/or clinical protocols should be centered on the purpose and underlying theory of each model.
Pubmed ID: 29694609
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An independent pan-European clinical research organization to improve the standards of cancer care through the multidisciplinary multinational efforts of basic scientists and clinicians. The efforts include the testing of more effective therapeutic strategies based on drugs, surgery and/or radiotherapy that are already in use. They also contribute to the development of new drugs and other approaches in partnership with the pharmaceutical industry which is accomplished mainly by conducting large, multicenter, prospective, randomized, phase III clinical trials. The EORTC Network comprises over 300 hospitals and cancer centers in over 30 countries which include some 2,500 collaborators from all disciplines involved in cancer treatment and research. The EORTC Headquarters staff handle some 6,000 new patients enrolled each year in cancer clinical trials, approximately 30 protocols that are permanently open to patient entry, over 50,000 patients who are in follow-up, and a database of more than 180,000 patients. Intergroup collaboration is also promoted to face current challenges of clinical trials aiming at targeted therapies in order to recruit a large number of patients within a reasonable period of time.
View all literature mentionsStatistical modeling program that provides a wide choice of models, estimators, and algorithms in a program that has graphical displays of data and analysis results. Mplus allows the analysis of both cross-sectional and longitudinal data, single-level and multilevel data, data that come from different populations with either observed or unobserved heterogeneity, and data that contain missing values. Analyses can be carried out for observed variables that are continuous, censored, binary, ordered categorical (ordinal), unordered categorical (nominal), counts, or combinations of these variable types. In addition, Mplus has extensive capabilities for Monte Carlo simulation studies, where data can be generated and analyzed according to any of the models included in the program. The Mplus modeling framework draws on the unifying theme of latent variables. The generality of the Mplus modeling framework comes from the unique use of both continuous and categorical latent variables. Continuous latent variables are used to represent factors corresponding to unobserved constructs, random effects corresponding to individual differences in development, random effects corresponding to variation in coefficients across groups in hierarchical data, frailties corresponding to unobserved heterogeneity in survival time, liabilities corresponding to genetic susceptibility to disease, and latent response variable values corresponding to missing data. Categorical latent variables are used to represent latent classes corresponding to homogeneous groups of individuals, latent trajectory classes corresponding to types of development in unobserved populations, mixture components corresponding to finite mixtures of unobserved populations, and latent response variable categories corresponding to missing data.
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