{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T00:50:00Z","timestamp":1721782200887},"reference-count":27,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"3","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["JCO Clinical Cancer Informatics"],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:sec><jats:title>PURPOSE<\/jats:title><jats:p> Data collection in clinical trials is becoming complex, with a huge number of variables that need to be recorded, verified, and analyzed to effectively measure clinical outcomes. In this study, we used data warehouse (DW) concepts to achieve this goal. A DW was developed to accommodate data from a large clinical trial, including all the characteristics collected. We present the results related to baseline variables with the following objectives: developing a data quality (DQ) control strategy and improving outcome analysis according to the clinical trial primary end points. <\/jats:p><\/jats:sec><jats:sec><jats:title>METHODS<\/jats:title><jats:p> Data were retrieved from the electronic case reporting forms (eCRFs) of the phase III, multicenter MCL0208 trial (ClinicalTrials.gov identifier: NCT02354313 ) of the Fondazione Italiana Linfomi for younger patients with untreated mantle cell lymphoma (MCL). The DW was created with a relational database management system. Recommended DQ dimensions were observed to monitor the activity of each site to handle DQ management during patient follow-up. The DQ management was applied to clinically relevant parameters that predicted progression-free survival to assess its impact. <\/jats:p><\/jats:sec><jats:sec><jats:title>RESULTS<\/jats:title><jats:p> The DW encompassed 16 tables, which included 226 variables for 300 patients and 199,500 items of data. The tool allowed cross-comparison analysis and detected some incongruities in eCRFs, prompting queries to clinical centers. This had an impact on clinical end points, as the DQ control strategy was able to improve the prognostic stratification according to single parameters, such as tumor infiltration by flow cytometry, and even using established prognosticators, such as the MCL International Prognostic Index. <\/jats:p><\/jats:sec><jats:sec><jats:title>CONCLUSION<\/jats:title><jats:p> The DW is a powerful tool to organize results from large phase III clinical trials and to effectively improve DQ through the application of effective engineered tools. <\/jats:p><\/jats:sec>","DOI":"10.1200\/cci.19.00049","type":"journal-article","created":{"date-parts":[[2019,10,21]],"date-time":"2019-10-21T20:04:01Z","timestamp":1571688241000},"page":"1-15","update-policy":"http:\/\/dx.doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":7,"title":["Applying Data Warehousing to a Phase III Clinical Trial From the Fondazione Italiana Linfomi Ensures Superior Data Quality and Improved Assessment of 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