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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
Published on: September 20, 2018
Data cleaning for clinician researchers: Application and explanation of a data-quality framework
Julia K Pilowsky1, Rosalind Elliott2, Michael A Roche3
1Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia; Faculty of Health, University of Technology Sydney, Sydney, NSW, Australia; Royal North Shore Hospital, Northern Sydney Local Health District, Sydney, NSW, Australia.
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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