ArticleJournal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing2017
Feasibility of Combining Common Data Elements Across Studies to Test a Hypothesis.
Article in Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 17 citations in OpenAlex.
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- Advancing Global Cancer Symptom Science: Insights and Strategies from the Inaugural Cancer Symptom Science Expert Meeting.Seminars in oncology nursing · 2025Article
- Improving Rural Healthcare in Mobile Clinics: Real-Time, Live Data Entry into the Electronic Medical Record Using a Satellite Internet Connection.International journal of environmental research and public health · 2025Article
- The case for open science: rare diseases.JAMIA open · 2020Review
- Call for Data Standardization: Lessons Learned and Recommendations in an Imaging Study.JCO clinical cancer informatics · 2019Article
- Chronic kidney diseases in agricultural communities: report from a workshop.Kidney international · 2019Article
- Symptom Science: Advocating for Inclusion of Functional Genetic Polymorphisms.Biological research for nursing · 2019Review
- Challenges to the Standardization of Trauma Data Collection in Burn, Traumatic Brain Injury, Spinal Cord Injury, and Other Trauma Populations: A Call for Common Data Elements for Acute and Longitudinal Trauma Databases.Archives of physical medicine and rehabilitation · 2019Article
Corrections and comments
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Authors and funding
14 authors at 7 institutions in 1 country.
Funding
Abstract
purposeThe purpose of this article is to describe the outcomes of a collaborative initiative to share data across five schools of nursing in order to evaluate the feasibility of collecting common data elements (CDEs) and developing a common data repository to test hypotheses of interest to nursing scientists. This initiative extended work already completed by the National Institute of Nursing Research CDE Working Group that successfully identified CDEs related to symptoms and self-management, with the goal of supporting more complex, reproducible, and patient-focused research.
designTwo exemplars describing the group's efforts are presented. The first highlights a pilot study wherein data sets from various studies by the represented schools were collected retrospectively, and merging of the CDEs was attempted. The second exemplar describes the methods and results of an initiative at one school that utilized a prospective design for the collection and merging of CDEs.
methodsMethods for identifying a common symptom to be studied across schools and for collecting the data dictionaries for the related data elements are presented for the first exemplar. The processes for defining and comparing the concepts and acceptable values, and for evaluating the potential to combine and compare the data elements are also described. Presented next are the steps undertaken in the second exemplar to prospectively identify CDEs and establish the data dictionaries. Methods for common measurement and analysis strategies are included.
findingsFindings from the first exemplar indicated that without plans in place a priori to ensure the ability to combine and compare data from disparate sources, doing so retrospectively may not be possible, and as a result hypothesis testing across studies may be prohibited. Findings from the second exemplar, however, indicated that a plan developed prospectively to combine and compare data sets is feasible and conducive to merged hypothesis testing.
conclusionsAlthough challenges exist in combining CDEs across studies into a common data repository, a prospective, well-designed protocol for identifying, coding, and comparing CDEs is feasible and supports the development of a common data repository and the testing of important hypotheses to advance nursing science. CLINICAL RELEVANCE: Incorporating CDEs across studies will increase sample size and improve data validity, reliability, transparency, and reproducibility, all of which will increase the scientific rigor of the study and the likelihood of impacting clinical practice and patient care.
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