Data quality assessment of interventional trials in public trial databases

Abstract

ObjectiveHigh-quality data entry in clinical trial databases is crucial to the usefulness, validity, and replicability of research findings, as it influences evidence-based medical practice and future research. Our aim is to assess the quality of self-reported data in trial registries and present practical and systematic methods for identifying and evaluating data quality.

Study Design and SettingWe searched ClinicalTrials.Gov (CTG) for interventional total knee arthroplasty (TKA) trials between 2000 and 2015. We extracted required and optional trial information elements and used the CTG's variables' definitions. We performed a literature review on data quality reporting on frameworks, checklists, and overviews of irregularities in healthcare databases. We identified and assessed data quality attributes as follows: consistency, accuracy, completeness, and timeliness.

ResultsWe included 816 interventional TKA trials. Data irregularities varied widely: 0%–100%. Inconsistency ranged from 0% to 36%, and most often nonrandomized labeled allocation was combined with a “single-group” assignment trial design. Inaccuracy ranged from 0% to 100%. Incompleteness ranged from 0% to 61%; 61% of finished TKA trials did not report their outcome. With regard to irregularities in timeliness, 49% of the trials were registered more than 3 months after the start date. ConclusionWe found significant variations in the data quality of registered clinical TKA trials. Trial sponsors should be committed to ensuring that the information they provide is reliable, consistent, up-to-date, transparent, and accurate. CTG's users need to be critical when drawing conclusions based on the registered data. We believe this awareness will increase well-informed decisions about published articles and treatment protocols, including replicating and improving trial designs.