Applying data warehousing to a phase III clinical trial from the Fondazione Italiana Linfomi (FIL) ensures superior data quality and improved assessment of clinical outcomes

PURPOSE. Data collection in clinical trials is becoming complex, with huge amounts of variables that need to be recorded, verified, and analyzed to effectively measure clinical outcomes. In this study, we employed 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: 1) developing a data quality (DQ) control strategy and 2) improved outcome analysis according to the clinical trial primary endpoints. PATIENTS AND METHODS. Data were retrieved from the electronic case reporting forms (eCRFs) of the phase III, multicenter MCL0208 trial (NCT02354313) of the Fondazione Italiana Linfomi for younger, untreated mantle cell lymphoma (MCL) patients. The DW was created with a relational database management system. Reccomended DQ dimensions were observed to monitor the activity of each site to handle DQ management during the patient follow-up. The DQ management was applied to clinically relevant parameters that predicted progression-free survival to assess its impact. RESULTS. 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 endpoints 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. CONCLUSION. 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.