Data cleaning for clinician researchers: Application and explanation of a data-quality framework.

Pilowsky, JK; Elliott, R; Roche, MA

Data cleaning for clinician researchers: Application and explanation of a data-quality framework.

Pilowsky, JK Elliott, R

Roche, MA

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Publisher:: ELSEVIER SCIENCE INC
Publication Type:: Journal Article
Citation:: Aust Crit Care, 2024, 37, (5), pp. 827-833
Issue Date:: 2024-09

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Field	Value	Language
dc.contributor.author	Pilowsky, JK
dc.contributor.author	Elliott, R https://orcid.org/0000-0002-9239-7126
dc.contributor.author	Roche, MA
dc.date.accessioned	2024-09-30T23:17:05Z
dc.date.available	2024-03-10
dc.date.available	2024-09-30T23:17:05Z
dc.date.issued	2024-09
dc.identifier.citation	Aust Crit Care, 2024, 37, (5), pp. 827-833
dc.identifier.issn	1036-7314
dc.identifier.issn	1878-1721
dc.identifier.uri	http://hdl.handle.net/10453/181114
dc.description.abstract	BACKGROUND: Data cleaning is the series of procedures performed before a formal statistical analysis, with the aim of reducing the number of error values in a dataset and improving the overall quality of subsequent analyses. Several study-reporting guidelines recommend the inclusion of data-cleaning procedures; however, little practical guidance exists for how to conduct these procedures. OBJECTIVES: This paper aimed to provide practical guidance for how to perform and report rigorous data-cleaning procedures. METHODS: A previously proposed data-quality framework was identified and used to facilitate the description and explanation of data-cleaning procedures. The broader data-cleaning process was broken down into discrete tasks to create a data-cleaning checklist. Examples of the how the various tasks had been undertaken for a previous study using data from the Australia and New Zealand Intensive Care Society Adult Patient Database were also provided. RESULTS: Data-cleaning tasks were described and grouped according to four data-quality domains described in the framework: data integrity, consistency, completeness, and accuracy. Tasks described include creation of a data dictionary, checking consistency of values across multiple variables, quantifying and managing missing data, and the identification and management of outlier values. The data-cleaning task checklist provides a practical summary of the various aspects of the data-cleaning process and will assist clinician researchers in performing this process in the future. CONCLUSIONS: Data cleaning is an integral part of any statistical analysis and helps ensure that study results are valid and reproducible. Use of the data-cleaning task checklist will facilitate the conduct of rigorous data-cleaning processes, with the aim of improving the quality of future research.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	ELSEVIER SCIENCE INC
dc.relation.ispartof	Aust Crit Care
dc.relation.isbasedon	10.1016/j.aucc.2024.03.004
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	1103 Clinical Sciences, 1110 Nursing
dc.subject.classification	Nursing
dc.subject.classification	4205 Nursing
dc.subject.mesh	Humans
dc.subject.mesh	Checklist
dc.subject.mesh	Data Accuracy
dc.subject.mesh	Research Design
dc.subject.mesh	Data Interpretation, Statistical
dc.subject.mesh	Australia
dc.subject.mesh	New Zealand
dc.subject.mesh	Humans
dc.subject.mesh	Data Interpretation, Statistical
dc.subject.mesh	Research Design
dc.subject.mesh	Australia
dc.subject.mesh	New Zealand
dc.subject.mesh	Checklist
dc.subject.mesh	Data Accuracy
dc.subject.mesh	Humans
dc.subject.mesh	Checklist
dc.subject.mesh	Data Accuracy
dc.subject.mesh	Research Design
dc.subject.mesh	Data Interpretation, Statistical
dc.subject.mesh	Australia
dc.subject.mesh	New Zealand
dc.title	Data cleaning for clinician researchers: Application and explanation of a data-quality framework.
dc.type	Journal Article
utslib.citation.volume	37
utslib.location.activity	Australia
utslib.for	1103 Clinical Sciences
utslib.for	1110 Nursing
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Health
pubs.organisational-group	University of Technology Sydney/Faculty of Health/School of Nursing and Midwifery
utslib.copyright.status	closed_access	*
dc.date.updated	2024-09-30T23:17:03Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	37
utslib.citation.issue	5

Abstract:

BACKGROUND: Data cleaning is the series of procedures performed before a formal statistical analysis, with the aim of reducing the number of error values in a dataset and improving the overall quality of subsequent analyses. Several study-reporting guidelines recommend the inclusion of data-cleaning procedures; however, little practical guidance exists for how to conduct these procedures. OBJECTIVES: This paper aimed to provide practical guidance for how to perform and report rigorous data-cleaning procedures. METHODS: A previously proposed data-quality framework was identified and used to facilitate the description and explanation of data-cleaning procedures. The broader data-cleaning process was broken down into discrete tasks to create a data-cleaning checklist. Examples of the how the various tasks had been undertaken for a previous study using data from the Australia and New Zealand Intensive Care Society Adult Patient Database were also provided. RESULTS: Data-cleaning tasks were described and grouped according to four data-quality domains described in the framework: data integrity, consistency, completeness, and accuracy. Tasks described include creation of a data dictionary, checking consistency of values across multiple variables, quantifying and managing missing data, and the identification and management of outlier values. The data-cleaning task checklist provides a practical summary of the various aspects of the data-cleaning process and will assist clinician researchers in performing this process in the future. CONCLUSIONS: Data cleaning is an integral part of any statistical analysis and helps ensure that study results are valid and reproducible. Use of the data-cleaning task checklist will facilitate the conduct of rigorous data-cleaning processes, with the aim of improving the quality of future research.

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http://hdl.handle.net/10453/181114