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ODM Data Analysis—A tool for the automatic validation, monitoring and generation of generic descriptive statistics of patient data

INTRODUCTION: A required step for presenting results of clinical studies is the declaration of participants demographic and baseline characteristics as claimed by the FDAAA 801. The common workflow to accomplish this task is to export the clinical data from the used electronic data capture system an...

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Autores principales: Brix, Tobias Johannes, Bruland, Philipp, Sarfraz, Saad, Ernsting, Jan, Neuhaus, Philipp, Storck, Michael, Doods, Justin, Ständer, Sonja, Dugas, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6014674/
https://www.ncbi.nlm.nih.gov/pubmed/29933373
http://dx.doi.org/10.1371/journal.pone.0199242
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author Brix, Tobias Johannes
Bruland, Philipp
Sarfraz, Saad
Ernsting, Jan
Neuhaus, Philipp
Storck, Michael
Doods, Justin
Ständer, Sonja
Dugas, Martin
author_facet Brix, Tobias Johannes
Bruland, Philipp
Sarfraz, Saad
Ernsting, Jan
Neuhaus, Philipp
Storck, Michael
Doods, Justin
Ständer, Sonja
Dugas, Martin
author_sort Brix, Tobias Johannes
collection PubMed
description INTRODUCTION: A required step for presenting results of clinical studies is the declaration of participants demographic and baseline characteristics as claimed by the FDAAA 801. The common workflow to accomplish this task is to export the clinical data from the used electronic data capture system and import it into statistical software like SAS software or IBM SPSS. This software requires trained users, who have to implement the analysis individually for each item. These expenditures may become an obstacle for small studies. Objective of this work is to design, implement and evaluate an open source application, called ODM Data Analysis, for the semi-automatic analysis of clinical study data. METHODS: The system requires clinical data in the CDISC Operational Data Model format. After uploading the file, its syntax and data type conformity of the collected data is validated. The completeness of the study data is determined and basic statistics, including illustrative charts for each item, are generated. Datasets from four clinical studies have been used to evaluate the application’s performance and functionality. RESULTS: The system is implemented as an open source web application (available at https://odmanalysis.uni-muenster.de) and also provided as Docker image which enables an easy distribution and installation on local systems. Study data is only stored in the application as long as the calculations are performed which is compliant with data protection endeavors. Analysis times are below half an hour, even for larger studies with over 6000 subjects. DISCUSSION: Medical experts have ensured the usefulness of this application to grant an overview of their collected study data for monitoring purposes and to generate descriptive statistics without further user interaction. The semi-automatic analysis has its limitations and cannot replace the complex analysis of statisticians, but it can be used as a starting point for their examination and reporting.
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spelling pubmed-60146742018-07-06 ODM Data Analysis—A tool for the automatic validation, monitoring and generation of generic descriptive statistics of patient data Brix, Tobias Johannes Bruland, Philipp Sarfraz, Saad Ernsting, Jan Neuhaus, Philipp Storck, Michael Doods, Justin Ständer, Sonja Dugas, Martin PLoS One Research Article INTRODUCTION: A required step for presenting results of clinical studies is the declaration of participants demographic and baseline characteristics as claimed by the FDAAA 801. The common workflow to accomplish this task is to export the clinical data from the used electronic data capture system and import it into statistical software like SAS software or IBM SPSS. This software requires trained users, who have to implement the analysis individually for each item. These expenditures may become an obstacle for small studies. Objective of this work is to design, implement and evaluate an open source application, called ODM Data Analysis, for the semi-automatic analysis of clinical study data. METHODS: The system requires clinical data in the CDISC Operational Data Model format. After uploading the file, its syntax and data type conformity of the collected data is validated. The completeness of the study data is determined and basic statistics, including illustrative charts for each item, are generated. Datasets from four clinical studies have been used to evaluate the application’s performance and functionality. RESULTS: The system is implemented as an open source web application (available at https://odmanalysis.uni-muenster.de) and also provided as Docker image which enables an easy distribution and installation on local systems. Study data is only stored in the application as long as the calculations are performed which is compliant with data protection endeavors. Analysis times are below half an hour, even for larger studies with over 6000 subjects. DISCUSSION: Medical experts have ensured the usefulness of this application to grant an overview of their collected study data for monitoring purposes and to generate descriptive statistics without further user interaction. The semi-automatic analysis has its limitations and cannot replace the complex analysis of statisticians, but it can be used as a starting point for their examination and reporting. Public Library of Science 2018-06-22 /pmc/articles/PMC6014674/ /pubmed/29933373 http://dx.doi.org/10.1371/journal.pone.0199242 Text en © 2018 Brix et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Brix, Tobias Johannes
Bruland, Philipp
Sarfraz, Saad
Ernsting, Jan
Neuhaus, Philipp
Storck, Michael
Doods, Justin
Ständer, Sonja
Dugas, Martin
ODM Data Analysis—A tool for the automatic validation, monitoring and generation of generic descriptive statistics of patient data
title ODM Data Analysis—A tool for the automatic validation, monitoring and generation of generic descriptive statistics of patient data
title_full ODM Data Analysis—A tool for the automatic validation, monitoring and generation of generic descriptive statistics of patient data
title_fullStr ODM Data Analysis—A tool for the automatic validation, monitoring and generation of generic descriptive statistics of patient data
title_full_unstemmed ODM Data Analysis—A tool for the automatic validation, monitoring and generation of generic descriptive statistics of patient data
title_short ODM Data Analysis—A tool for the automatic validation, monitoring and generation of generic descriptive statistics of patient data
title_sort odm data analysis—a tool for the automatic validation, monitoring and generation of generic descriptive statistics of patient data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6014674/
https://www.ncbi.nlm.nih.gov/pubmed/29933373
http://dx.doi.org/10.1371/journal.pone.0199242
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