Premium
Feasibility of Combining Common Data Elements Across Studies to Test a Hypothesis
Author(s) -
Corwin Elizabeth J.,
Moore Shirley M.,
Plotsky Andrea,
Heitkemper Margaret M.,
Dorsey Susan G.,
WaldropValverde Drenna,
Bailey Donald E.,
Docherty Sharron L.,
Whitney Joanne D.,
Musil Carol M.,
Dougherty Cynthia M.,
McCloskey Donna J.,
Austin Joan K.,
Grady Patricia A.
Publication year - 2017
Publication title -
journal of nursing scholarship
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.009
H-Index - 80
eISSN - 1547-5069
pISSN - 1527-6546
DOI - 10.1111/jnu.12287
Subject(s) - test (biology) , data collection , computer science , nursing research , data science , research design , common ground , psychology , medicine , nursing , social psychology , mathematics , statistics , paleontology , biology
Abstract Purpose The 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. Design Two 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. Methods Methods 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. Findings Findings 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. Conclusions Although 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.