Literature DB >> 30510791

How to set up a database?-a five-step process.

Alice Brembilla1,2, Bérenger Martin1, Anne-Laure Parmentier1,2, Maxime Desmarets1,3, Pierre-Emmanuel Falcoz4,5,6, Marc Puyraveau1,2, Frédéric Mauny1,2.   

Abstract

Database set-up directly impacts the quality and viability of research data, and therefore is a crucial part of the quality of clinical research. Setting up a quality database implies following a strict data-management process. Too much collected information threatens the quality of the information required to achieve the objectives of the study. Therefore, the data that will be collected and managed have to be cautiously discussed and selected. Case report forms (CRF) are the tools the most frequently used to collect the data specified by the protocol. An informative and well-structured document simplifies database design and data validation. Key elements are about choice of sequential or thematic structuring, information and type of information that should be entered and the importance of data standards and coding guide. Final database must be structured with unique ID patient, with one record per subject or per measure. Specific information must be provided for each variable according to the database specifications. The quality of the results is directly related to the quality of the collected data. The CRF should then be completed as fully and accurately as possible. Data validation relies on three key points: the CRF completion guidelines, the Edit Checks process and the Data clarification process. Various open source or business software applications provide all functionalities to set up a clinical data base and CRF. The General Data Protection Regulation (GDPR) standardizes and strengthens the protection of personal data across the EU and for other country's data being "processed" within the EU. The General principles include lawfulness, fairness and transparency, restricted use of data, data minimization, accuracy, limited storage, confidentiality and probity, and accountability.

Entities:  

Keywords:  Database; General Data Protection Regulation (GDPR); software

Year:  2018        PMID: 30510791      PMCID: PMC6230828          DOI: 10.21037/jtd.2018.09.138

Source DB:  PubMed          Journal:  J Thorac Dis        ISSN: 2072-1439            Impact factor:   2.895


  6 in total

1.  CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials.

Authors:  David Moher; Sally Hopewell; Kenneth F Schulz; Victor Montori; Peter C Gøtzsche; P J Devereaux; Diana Elbourne; Matthias Egger; Douglas G Altman
Journal:  BMJ       Date:  2010-03-23

2.  The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies.

Authors:  Erik von Elm; Douglas G Altman; Matthias Egger; Stuart J Pocock; Peter C Gøtzsche; Jan P Vandenbroucke
Journal:  Int J Surg       Date:  2014-07-18       Impact factor: 6.071

3.  Everyone needs a data-management plan.

Authors: 
Journal:  Nature       Date:  2018-03-15       Impact factor: 49.962

4.  Data management made simple.

Authors:  Quirin Schiermeier
Journal:  Nature       Date:  2018-03-15       Impact factor: 49.962

Review 5.  Big Data, Big Problems: A Healthcare Perspective.

Authors:  Mowafa S Househ; Bakheet Aldosari; Abdullah Alanazi; Andre W Kushniruk; Elizabeth M Borycki
Journal:  Stud Health Technol Inform       Date:  2017

6.  Data management in clinical research: An overview.

Authors:  Binny Krishnankutty; Shantala Bellary; Naveen B R Kumar; Latha S Moodahadu
Journal:  Indian J Pharmacol       Date:  2012-03       Impact factor: 1.200

  6 in total
  1 in total

1.  A semi-automated pipeline for fulfillment of resource requests from a longitudinal Alzheimer's disease registry.

Authors:  Katelyn A McKenzie; Suzanne L Hunt; Genevieve Hulshof; Dinesh Pal Mudaranthakam; Kayla Meyer; Eric D Vidoni; Jeffrey M Burns; Jonathan D Mahnken
Journal:  JAMIA Open       Date:  2019-08-26
  1 in total

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