PURPOSE: 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. METHODS: 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. 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 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. 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.
PURPOSE: 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. METHODS: 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. 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 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. 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.
Authors: Paul A Harris; Robert Taylor; Robert Thielke; Jonathon Payne; Nathaniel Gonzalez; Jose G Conde Journal: J Biomed Inform Date: 2008-09-30 Impact factor: 6.317
Authors: Eva Hoster; Martin Dreyling; Wolfram Klapper; Christian Gisselbrecht; Achiel van Hoof; Hanneke C Kluin-Nelemans; Michael Pfreundschuh; Marcel Reiser; Bernd Metzner; Hermann Einsele; Norma Peter; Wolfram Jung; Bernhard Wörmann; Wolf-Dieter Ludwig; Ulrich Dührsen; Hartmut Eimermacher; Hannes Wandt; Joerg Hasford; Wolfgang Hiddemann; Michael Unterhalt Journal: Blood Date: 2007-10-25 Impact factor: 22.113
Authors: Jonas Scherer; Marco Nolden; Jens Kleesiek; Jasmin Metzger; Klaus Kades; Verena Schneider; Michael Bach; Oliver Sedlaczek; Andreas M Bucher; Thomas J Vogl; Frank Grünwald; Jens-Peter Kühn; Ralf-Thorsten Hoffmann; Jörg Kotzerke; Oliver Bethge; Lars Schimmöller; Gerald Antoch; Hans-Wilhelm Müller; Andreas Daul; Konstantin Nikolaou; Christian la Fougère; Wolfgang G Kunz; Michael Ingrisch; Balthasar Schachtner; Jens Ricke; Peter Bartenstein; Felix Nensa; Alexander Radbruch; Lale Umutlu; Michael Forsting; Robert Seifert; Ken Herrmann; Philipp Mayer; Hans-Ulrich Kauczor; Tobias Penzkofer; Bernd Hamm; Winfried Brenner; Roman Kloeckner; Christoph Düber; Mathias Schreckenberger; Rickmer Braren; Georgios Kaissis; Marcus Makowski; Matthias Eiber; Andrei Gafita; Rupert Trager; Wolfgang A Weber; Jakob Neubauer; Marco Reisert; Michael Bock; Fabian Bamberg; Jürgen Hennig; Philipp Tobias Meyer; Juri Ruf; Uwe Haberkorn; Stefan O Schoenberg; Tristan Kuder; Peter Neher; Ralf Floca; Heinz-Peter Schlemmer; Klaus Maier-Hein Journal: JCO Clin Cancer Inform Date: 2020-11