Improving Research Data Integrity: Applying Novel Tools in a Longitudinal Breast Cancer Clinical Trial

2.50
Hdl Handle:
http://hdl.handle.net/10755/165349
Category:
Abstract
Type:
Presentation
Title:
Improving Research Data Integrity: Applying Novel Tools in a Longitudinal Breast Cancer Clinical Trial
Author(s):
McNees, P.; Hassey-Dow, K.; Ramaswamysanthanam, S.; Subramanian, G.; Wochna-Loerzel, V.
Author Details:
P. McNees, Applied Health Science, Orlando, Florida, USA; K. Hassey-Dow; S. Ramaswamysanthanam; G. Subramanian; V. Wochna-Loerzel
Abstract:
Improving data integrity is a process and not an event. Yet methodologies that systematically improve most research processes and produce higher quality research data have not been specified or systematically evaluated. Longitudinal clinical trials present particularly salient challenges that can limit analyses, threaten interpretation and/or conclusions drawn from the data. Purpose: The objectives of this paper are to (a) describe the application of engineering quality improvement techniques to a longitudinal quality of life (QOL) clinical trial, and (b) determine the impact of engineering techniques to maintain and improve data integrity. Theoretical/Scientific Framework: Deming’s quality improvement framework and principles of statistical process control form the theoretical underpinnings for this work. Methods: The investigators are conducting an ongoing randomized QOL clinical trial that will accrue 250 subjects. Subjects have either 6 or 7 monthly data accrual points. Based on initial results of the first quality audit of 50 subjects’ data, the investigators identified improvements needed, and designed and implemented a novel and systematic approach to full quality improvement. This process included application of engineering techniques such as: statistical process control, item sampling, data review, quality audit, and feedback control. Data Analysis: A behavioral observational model was paired with statistical process analyses for both informing the research processes and performing analyses. The specific formula for estimating reliability was r.coeffecient=[(agreements)/(agreements+disagreements). Other data were tabulated from data entry records. Findings and Implications: The techniques used in this study resulted in incremental improvements including: greater inter-rater reliability, decreased error in missing data, improved data entry, enhanced data flow coordination, and reduced person hours involved in data management. Baseline reliability was 0.9676. While relatively high, analysis of first 110 subjects, reflect a 41% reduction in data errors from baseline. Thus, applying quality improvement engineering techniques and focusing on controllable sources of variability resulted in significantly fewer errors and improved data quality and integrity. Improving quality or data integrity is not an event, but a process. As such, application of engineering quality control techniques can result in improvement towards error-free data, while simultaneously providing an ongoing system for continuing to improve future research projects.
Repository Posting Date:
27-Oct-2011
Date of Publication:
27-Oct-2011
Conference Date:
2005
Conference Name:
30th Annual Oncology Nursing Society Congress
Conference Host:
Oncology Nursing Society
Conference Location:
Orlando, Florida, USA
Note:
This is an abstract-only submission. If the author has submitted a full-text item based on this abstract, you may find it by browsing the Virginia Henderson Global Nursing e-Repository by author. If author contact information is available in this abstract, please feel free to contact him or her with your queries regarding this submission. Alternatively, please contact the conference host, journal, or publisher (according to the circumstance) for further details regarding this item. If a citation is listed in this record, the item has been published and is available via open-access avenues or a journal/database subscription. Contact your library for assistance in obtaining the as-published article.

Full metadata record

DC FieldValue Language
dc.type.categoryAbstracten_US
dc.typePresentationen_GB
dc.titleImproving Research Data Integrity: Applying Novel Tools in a Longitudinal Breast Cancer Clinical Trialen_GB
dc.contributor.authorMcNees, P.en_US
dc.contributor.authorHassey-Dow, K.en_US
dc.contributor.authorRamaswamysanthanam, S.en_US
dc.contributor.authorSubramanian, G.en_US
dc.contributor.authorWochna-Loerzel, V.en_US
dc.author.detailsP. McNees, Applied Health Science, Orlando, Florida, USA; K. Hassey-Dow; S. Ramaswamysanthanam; G. Subramanian; V. Wochna-Loerzelen_US
dc.identifier.urihttp://hdl.handle.net/10755/165349-
dc.description.abstractImproving data integrity is a process and not an event. Yet methodologies that systematically improve most research processes and produce higher quality research data have not been specified or systematically evaluated. Longitudinal clinical trials present particularly salient challenges that can limit analyses, threaten interpretation and/or conclusions drawn from the data. Purpose: The objectives of this paper are to (a) describe the application of engineering quality improvement techniques to a longitudinal quality of life (QOL) clinical trial, and (b) determine the impact of engineering techniques to maintain and improve data integrity. Theoretical/Scientific Framework: Deming’s quality improvement framework and principles of statistical process control form the theoretical underpinnings for this work. Methods: The investigators are conducting an ongoing randomized QOL clinical trial that will accrue 250 subjects. Subjects have either 6 or 7 monthly data accrual points. Based on initial results of the first quality audit of 50 subjects’ data, the investigators identified improvements needed, and designed and implemented a novel and systematic approach to full quality improvement. This process included application of engineering techniques such as: statistical process control, item sampling, data review, quality audit, and feedback control. Data Analysis: A behavioral observational model was paired with statistical process analyses for both informing the research processes and performing analyses. The specific formula for estimating reliability was r.coeffecient=[(agreements)/(agreements+disagreements). Other data were tabulated from data entry records. Findings and Implications: The techniques used in this study resulted in incremental improvements including: greater inter-rater reliability, decreased error in missing data, improved data entry, enhanced data flow coordination, and reduced person hours involved in data management. Baseline reliability was 0.9676. While relatively high, analysis of first 110 subjects, reflect a 41% reduction in data errors from baseline. Thus, applying quality improvement engineering techniques and focusing on controllable sources of variability resulted in significantly fewer errors and improved data quality and integrity. Improving quality or data integrity is not an event, but a process. As such, application of engineering quality control techniques can result in improvement towards error-free data, while simultaneously providing an ongoing system for continuing to improve future research projects.en_GB
dc.date.available2011-10-27T12:16:57Z-
dc.date.issued2011-10-27en_GB
dc.date.accessioned2011-10-27T12:16:57Z-
dc.conference.date2005en_US
dc.conference.name30th Annual Oncology Nursing Society Congressen_US
dc.conference.hostOncology Nursing Societyen_US
dc.conference.locationOrlando, Florida, USAen_US
dc.description.noteThis is an abstract-only submission. If the author has submitted a full-text item based on this abstract, you may find it by browsing the Virginia Henderson Global Nursing e-Repository by author. If author contact information is available in this abstract, please feel free to contact him or her with your queries regarding this submission. Alternatively, please contact the conference host, journal, or publisher (according to the circumstance) for further details regarding this item. If a citation is listed in this record, the item has been published and is available via open-access avenues or a journal/database subscription. Contact your library for assistance in obtaining the as-published article.-
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