Data Quality Monitoring in Clinical Trials: Has It Been Worth It? An Evaluation and Prediction of the Future by All Stakeholders.
Year: 2016
Type: article
Abstract: OBJECTIVE: The increasing importance of real-world data for clinical and policy decision making is driving a need for close attention to the pragmatic versus explanatory features of trial designs. ASPECT-R (A Study Pragmatic-Explanatory Characterization Tool-Rating) is an instrument informed by the PRECIS tool, which was developed to assist researchers in designing trials that are more pragmatic or explanatory. ASPECT-R refined the PRECIS domains and includes a detailed anchored rating system. This analysis established the inter-rater reliability of ASPECT-R. DESIGN: Nine raters (identified from a convenience sample of persons knowledgeable about psychiatry clinical research/study design) received ASPECT-R training materials and 12 study publications. Selected studies assessed antipsychotic treatment in schizophrenia, were published in peer-reviewed journals, and represented a range of studies across a pragmatic-explanatory continuum as determined by authors (CB/LA). After completing training, raters reviewed the 12 studies and rated the study domains using ASPECT-R. Intraclass correlation coefficients were estimated for total and domain scores. Qualitative ratings then were assigned to describe the inter-rater reliability. RESULTS: ASPECT-R scores for the 12 studies were completed by seven raters. The ASPECT-R total score intraclass correlation coefficient was 0.87, corresponding to an excellent inter-rater reliability. Domain intraclass correlation coefficients ranged from 0.85 to 0.31, corresponding to excellent to poor inter-rater reliability. CONCLUSION: The inter-rater reliability of the ASPECT-R total score was excellent, with excellent to good inter-rater reliability for most domains. The fair to poor inter-rater reliability for two domains may reflect a need for improved domain definition, anchoring, or training materials. ASPECT-R can be used to help understand the pragmaticexplanatory nature of completed or planned trials.
Source: PubMed Central
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2518459831