scholarly journals Capturing a Stata Dataset and Releasing it into REDcap

2017 ◽  
Vol 17 (1) ◽  
pp. 130-138
Author(s):  
Seth T. Lirette ◽  
Samantha R. Seals ◽  
Chad Blackshear ◽  
Warren May
Keyword(s):  
Data Collection ◽  
Data Entry ◽  
Data Capture ◽  
Data Dictionary ◽  
Electronic Data Capture ◽  
Application Research ◽  
Electronic Data ◽  
Web Based ◽  
Internet Connection ◽  
Technology Advances

With technology advances, researchers can now capture data using web-based applications. One such application, Research Electronic Data Capture (REDCap), allows for data entry from any computer with an Internet connection. As the use of REDCap has increased in popularity, we have observed the need to easily create data dictionaries and data collection instruments for REDCap. The command presented in this article, redcapture, demonstrates one method to create a REDCap-ready data dictionary using a loaded Stata dataset, illustrated by examples of starting from an existing dataset or completely starting from scratch.

scholarly journals The experience of the clinical trial center at Alessandria Hospital regarding the activation of COVID-19 clinical trials during the pandemic

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Annalisa Roveta ◽  
Fabio Giacchero ◽  
Carolina Pelazza ◽  
Serena Penpa ◽  
Costanza Massarino ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Data Collection ◽  
Clinical Research ◽  
Fast Track ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Web Based ◽  
Taking Charge

Objective: The aim is to evaluate the speed in the activation of Covid-19 clinical trials at SS. Antonio e Biagio e Cesare Arrigo Hospital of Alessandria during the pandemic. Methods: Data collection related to the activation and the conduction of clinical trials was managed using a database created through a web-based platform REDCap (Research Electronic Data Capture). Results: 32 studies were activated in the period between March 23 and July 31, 2020. An average time of 14 days elapsed between taking charge of the request and the issuance of the authorization act. Conclusions: During the emergency it was possible to activate the trials quickly thanks to fast-track procedures, optimizing COVID-19 clinical research.

How Did a Multi-Institutional Trial Show Feasibility of Electronic Data Capture in Older Patients With Cancer? Results From a Multi-Institutional Qualitative Study (Alliance A171902)

2021 ◽  
pp. 442-449
Author(s):  
Nichole A. Martin ◽  
Elizabeth S. Harlos ◽  
Kathryn D. Cook ◽  
Jennifer M. O'Connor ◽  
Andrew Dodge ◽  
...  
Keyword(s):  
Older Patients ◽  
Electronic Devices ◽  
Data Entry ◽  
Data Capture ◽  
Data Availability ◽  
Electronic Data Capture ◽  
Wireless Internet ◽  
Electronic Data ◽  
Patients With Cancer ◽  
Patient Reported

PURPOSE New technology might pose problems for older patients with cancer. This study sought to understand how a trial in older patients with cancer (Alliance A171603) was successful in capturing electronic patient-reported data. METHODS Study personnel were invited via e-mail to participate in semistructured phone interviews, which were audio-recorded and qualitatively analyzed. RESULTS Twenty-four study personnel from the 10 sites were interviewed; three themes emerged. The first was that successful patient-reported electronic data capture shifted work toward patients and toward study personnel at the beginning of the study. One interviewee explained, “I mean it kind of lost all advantages…by being extremely laborious.” Study personnel described how they ensured electronic devices were charged, wireless internet access was up and running, and login codes were available. The second theme was related to the first and dealt with data filtering. Study personnel described high involvement in data gathering; for example, one interviewee described, “I answered on the iPad, whatever they said. They didn't even want to use it at all.” A third theme dealt with advantages of electronic data entry, such as prompt data availability at study completion. Surprisingly, some remarks described how electronic devices brought people together, “Some of the patients, you know, it just gave them a chance to kinda talk about, you know, what was going on.” CONCLUSION High rates of capture of patient-reported electronic data were viewed favorably but occurred in exchange for increased effort from patients and study personnel and in exchange for data that were not always patient-reported in the strictest sense.

scholarly journals Using REDCap to track stakeholder engagement: A time-saving tool for PCORI-funded studies

2020 ◽  
Vol 4 (2) ◽  
pp. 108-114
Author(s):  
Sabina B. Gesell ◽  
Jacqueline R. Halladay ◽  
Laurie H. Mettam ◽  
Mysha E. Sissine ◽  
B. Lynette Staplefoote-Boynton ◽  
...  
Keyword(s):  
Stakeholder Engagement ◽  
Data Transfer ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Patient Centered ◽  
Electronic Data ◽  
Time Saving ◽  
Web Based ◽  
Pragmatic Clinical Trials ◽  
Cluster Randomized

AbstractBackground:Research Electronic Data Capture (REDCap) is a secure, web-based electronic data capture application for building and managing surveys and databases. It can also be used for study management, data transfer, and data export into a variety of statistical programs. REDcap was developed and supported by the National Center for Advancing Translational Sciences Program and is used in over 3700 institutions worldwide. It can also be used to track and measure stakeholder engagement, an integral element of research funded by the Patient-Centered Outcomes Research Institute (PCORI). Continuously and accurately tracking and reporting on stakeholder engagement activities throughout the life of a PCORI-funded trial can be challenging, particularly in complex trials with multiple types of engagement.Methods:In this paper, we show our approach for collecting and capturing stakeholder engagement activities using a shareable REDCap tool in one of the PCORI’s first large pragmatic clinical trials (the Comprehensive Post-Acute Stroke Services) to inform other investigators planning cluster-randomized pragmatic trials. Benefits and challenges are highlighted for researchers seeking to consistently monitor and measure stakeholder engagement.Conclusions:We describe how REDCap can provide a time-saving approach to capturing how stakeholders engage in a PCORI-funded study and reporting how stakeholders influenced the study in progress reports back to PCORI.

Possible Combinations of Electronic Data Capture and Randomization Systems

2014 ◽  
Vol 53 (03) ◽  
pp. 202-207 ◽  
Author(s):  
M. Haag ◽  
L. R. Pilz ◽  
D. Schrimpf
Keyword(s):  
Open Source ◽  
Data Storage ◽  
Data Entry ◽  
Data Capture ◽  
Electronic Systems ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Training Costs ◽  
Pros And Cons ◽  
General Data

SummaryBackground: Clinical trials (CT) are in a wider sense experiments to prove and establish clinical benefit of treatments. Nowadays electronic data capture systems (EDCS) are used more often bringing a better data management and higher data quality into clinical practice. Also electronic systems for the randomization are used to assign the patients to the treatments.Objectives: If the mentioned randomization system (RS) and EDCS are used, possibly identical data are collected in both, especially by stratified randomization. This separated data storage may lead to data inconsistency and in general data samples have to be aligned. The article discusses solutions to combine RS and EDCS. In detail one approach is realized and introduced.Methods: Different possible settings of combination of EDCS and RS are determined and the pros and cons for each solution are worked out. For the combination of two independent applications the necessary interfaces for the communication are defined. Thereby, existing standards are considered. An example realization is implemented with the help of open-source applications and state-of-the-art software development procedures.Results: Three possibilities of separate usage or combination of EDCS and RS are pre -sented and assessed: i) the complete independent usage of both systems; ii) realization of one system with both functions; and iii) two separate systems, which communicate via defined interfaces. In addition a realization of our preferred approach, the combination of both systems, is introduced using the open source tools RANDI2 and Open-Clinica.Conclusion: The advantage of a flexible independent development of EDCS and RS is shown based on the fact that these tool are very different featured. In our opinion the combination of both systems via defined interfaces fulfills the requirements of randomization and electronic data capture and is feasible in practice. In addition, the use of such a setting can reduce the training costs and the error-prone duplicated data entry.

scholarly journals An Electronic Data Capture Framework (ConnEDCt) for Global and Public Health Research: Design and Implementation

10.2196/18580 ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. e18580 ◽  
Author(s):  
Caleb J Ruth ◽  
Samantha Lee Huey ◽  
Jesse T Krisher ◽  
Amy Fothergill ◽  
Bryan M Gannon ◽  
...  
Keyword(s):  
Clinical Research ◽  
Data Storage ◽  
Data Entry ◽  
Public Health Research ◽  
Ease Of Use ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Cross Sectional ◽  
Clinical Surveillance

Background When we were unable to identify an electronic data capture (EDC) package that supported our requirements for clinical research in resource-limited regions, we set out to build our own reusable EDC framework. We needed to capture data when offline, synchronize data on demand, and enforce strict eligibility requirements and complex longitudinal protocols. Based on previous experience, the geographical areas in which we conduct our research often have unreliable, slow internet access that would make web-based EDC platforms impractical. We were unwilling to fall back on paper-based data capture as we wanted other benefits of EDC. Therefore, we decided to build our own reusable software platform. In this paper, we describe our customizable EDC framework and highlight how we have used it in our ongoing surveillance programs, clinic-based cross-sectional studies, and randomized controlled trials (RCTs) in various settings in India and Ecuador. Objective This paper describes the creation of a mobile framework to support complex clinical research protocols in a variety of settings including clinical, surveillance, and RCTs. Methods We developed ConnEDCt, a mobile EDC framework for iOS devices and personal computers, using Claris FileMaker software for electronic data capture and data storage. Results ConnEDCt was tested in the field in our clinical, surveillance, and clinical trial research contexts in India and Ecuador and continuously refined for ease of use and optimization, including specific user roles; simultaneous synchronization across multiple locations; complex randomization schemes and informed consent processes; and collecting diverse types of data (laboratory, growth measurements, sociodemographic, health history, dietary recall and feeding practices, environmental exposures, and biological specimen collection). Conclusions ConnEDCt is customizable, with regulatory-compliant security, data synchronization, and other useful features for data collection in a variety of settings and study designs. Furthermore, ConnEDCt is user friendly and lowers the risks for errors in data entry because of real time error checking and protocol enforcement.

scholarly journals Use of research electronic data capture (REDCap) in a COVID-19 randomized controlled trial: a practical example

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sina Kianersi ◽  
Maya Luetke ◽  
Christina Ludema ◽  
Alexander Valenzuela ◽  
Molly Rosenberg
Keyword(s):  
Data Collection ◽  
Web Application ◽  
Controlled Trial ◽  
Antibody Test ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Protective Behavior ◽  
Electronic Data ◽  
Randomized Controlled ◽  
Longitudinal Data Collection

Abstract Background Randomized controlled trials (RCT) are considered the ideal design for evaluating the efficacy of interventions. However, conducting a successful RCT has technological and logistical challenges. Defects in randomization processes (e.g., allocation sequence concealment) and flawed masking could bias an RCT’s findings. Moreover, investigators need to address other logistics common to all study designs, such as study invitations, eligibility screening, consenting procedure, and data confidentiality protocols. Research Electronic Data Capture (REDCap) is a secure, browser-based web application widely used by researchers for survey data collection. REDCap offers unique features that can be used to conduct rigorous RCTs. Methods In September and November 2020, we conducted a parallel group RCT among Indiana University Bloomington (IUB) undergraduate students to understand if receiving the results of a SARS-CoV-2 antibody test changed the students’ self-reported protective behavior against coronavirus disease 2019 (COVID-19). In the current report, we discuss how we used REDCap to conduct the different components of this RCT. We further share our REDCap project XML file and instructional videos that investigators can use when designing and conducting their RCTs. Results We reported on the different features that REDCap offers to complete various parts of a large RCT, including sending study invitations and recruitment, eligibility screening, consenting procedures, lab visit appointment and reminders, data collection and confidentiality, randomization, blinding of treatment arm assignment, returning test results, and follow-up surveys. Conclusions REDCap offers powerful tools for longitudinal data collection and conduct of rigorous and successful RCTs. Investigators can make use of this electronic data capturing system to successfully complete their RCTs. Trial registration The RCT was prospectively (before completing data collection) registered at ClinicalTrials.gov; registration number: NCT04620798, date of registration: November 9, 2020.

scholarly journals Evaluation of Data Entry Errors and Data Changes to an Electronic Data Capture Clinical Trial Database

2011 ◽  
Vol 45 (4) ◽  
pp. 421-430 ◽  
Author(s):  
Jules T. Mitchel ◽  
Yong Joong Kim ◽  
Joonhyuk Choi ◽  
Glen Park ◽  
Silvana Cappi ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Data Entry ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Clinical Trial Database ◽  
Evaluation Of Data

scholarly journals Electronic data collection in epidemiological research

2016 ◽  
Vol 07 (03) ◽  
pp. 672-681 ◽  
Author(s):  
Aluísio Barros ◽  
Cauane Blumenberg
Keyword(s):  
Data Collection ◽  
Data Quality ◽  
Birth Cohort ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Epidemiological Research ◽  
Electronic Data Collection ◽  
And Control

SummaryThis paper describes the use of Research Electronic Data Capture (REDCap) to conduct one of the follow-up waves of the 2004 Pelotas birth cohort. The aim is to point out the advantages and limitations of using this electronic data capture environment to collect data and control every step of a longitudinal epidemiological research, specially in terms of time savings and data quality.We used REDCap as the main tool to support the conduction of a birth cohort follow-up. By exploiting several REDCap features, we managed to schedule assessments, collect data, and control the study workflow. To enhance data quality, we developed specific reports and field validations to depict inconsistencies in real time.Using REDCap it was possible to investigate more variables without significant increases on the data collection time, when comparing to a previous birth cohort follow-up. In addition, better data quality was achieved since negligible out of range errors and no validation or missing inconsistencies were identified after applying over 7,000 interviews.Adopting electronic data capture solutions, such as REDCap, in epidemiological research can bring several advantages over traditional paper-based data collection methods. In favor of improving their features, more research groups should migrate from paper to electronic-based epidemiological research.Citation: Blumenberg C, Barros AJD. Electronic data collection in epidemiological research: The use of REDCap in the Pelotas birth cohorts

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