Use of patient-reported outcomes in randomized, double-blind, placebo-controlled clinical trials.
ABSTRACT To optimize the use of patient-reported outcomes (PROs) in clinical research, it is first necessary to review the current use of these outcomes in clinical trials to determine under what circumstances they are most useful, and to reveal current limitations.
To investigate current patterns of use of PROs in clinical trials.
We conducted a systematic literature review of all double-blind, placebo-controlled, randomized clinical trials using one or more PROs as a study outcome from 2004 to 2006. Data were abstracted and analyzed with descriptive statistics and logistic regression to characterize the use of PROs in clinical trials.
The 180 clinical trials that met the study inclusion criteria used 173 unique instruments to measure a total of 466 PROs. Most PRO measurements were obtained using relatively few PRO instruments, with one-third of PRO instruments applied in more than 1 trial. In multivariable analysis, tests of statistical significance were more often reported for PROs used as primary trial outcomes. Statistically significant PRO outcomes (P<0.05) were more likely among disease-specific PROs compared with general PROs, PROs with a discussion of minimally important difference, and larger trials.
PRO instruments may be improved through efforts to provide centralized electronic administration, cross-validation, and standardized interpretation of clinically relevant outcomes. The majority of PROs used in current clinical trials come from relatively few, commonly used disease-specific PRO instruments within major therapeutic areas.
SourceAvailable from: Mona Dür, née Mathis[Show abstract] [Hide abstract]
ABSTRACT: Self-reported outcome instruments in health research have become increasingly important over the last decades. Occupational therapy interventions often focus on occupational balance. However, instruments to measure occupational balance are scarce. The aim of the study was therefore to develop a generic self-reported outcome instrument to assess occupational balance based on the experiences of patients and healthy people including an examination of its psychometric properties. We conducted a qualitative analysis of the life stories of 90 people with and without chronic autoimmune diseases to identify components of occupational balance. Based on these components, the Occupational Balance-Questionnaire (OB-Quest) was developed. Construct validity and internal consistency of the OB-Quest were examined in quantitative data. We used Rasch analyses to determine overall fit of the items to the Rasch model, person separation index and potential differential item functioning. Dimensionality testing was conducted by the use of t-tests and Cronbach's alpha. The following components emerged from the qualitative analyses: challenging and relaxing activities, activities with acknowledgement by the individual and by the sociocultural context, impact of health condition on activities, involvement in stressful activities and fewer stressing activities, rest and sleep, variety of activities, adaptation of activities according to changed living conditions and activities intended to care for oneself and for others. Based on these, the seven items of the questionnaire (OB-Quest) were developed. 251 people (132 with rheumatoid arthritis, 43 with systematic lupus erythematous and 76 healthy) filled in the OB-Quest. Dimensionality testing indicated multidimensionality of the questionnaire (t = 0.58, and 1.66 after item reduction, non-significant). The item on the component rest and sleep showed differential item functioning (health condition and age). Person separation index was 0.51. Cronbach's alpha changed from 0.38 to 0.57 after deleting two items. This questionnaire includes new items addressing components of occupational balance meaningful to patients and healthy people which have not been measured so far. The reduction of two items of the OB-Quest showed improved internal consistency. The multidimensionality of the questionnaire indicates the need for a summary of several components into subscales. http://www.hqlo.com/content/pdf/1477-7525-12-45.pdfHealth and Quality of Life Outcomes 04/2014; 12(1):45. DOI:10.1186/1477-7525-12-45 · 2.10 Impact Factor
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ABSTRACT: Objectives This article provides a commentary in response to “Varni et al. (Qual Life Res. doi:10.1007/s11136-013-0370-4, 2013)." Methods and results The commentary argues that the approximate model fit indexes commonly used in maximum-likelihood confirmatory factor analysis and factorial invariance testing are seriously flawed, as they overlook potentially serious model misspecifications that could bias parameter estimates and compromise inference. Conclusions Flexible and convenient Bayesian estimation approaches are presented that can substantially aid in: (1) resolving commonly encountered specification errors in confirmatory factor models and (2) locating specific measurement parameters that are non-invariant across population subgroups. It is recommended that these methods should be more widely adopted for evaluating the factorial invariance of patient-reported outcome measures and other types of instruments.Quality of Life Research 11/2013; DOI:10.1007/s11136-013-0465-y · 2.86 Impact Factor
American Journal of Respiratory and Critical Care Medicine 04/2014; 189(8):875-7. DOI:10.1164/rccm.201402-0362ED · 11.99 Impact Factor