Data cleaning for clinician researchers: Application and explanation of a data-quality framework.
Aust Crit Care
; 2024 Apr 09.
Article
en En
| MEDLINE
| ID: mdl-38600009
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.
Texto completo:
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Bases de datos:
MEDLINE
Idioma:
En
Revista:
Aust Crit Care
Asunto de la revista:
ENFERMAGEM
/
TERAPIA INTENSIVA
Año:
2024
Tipo del documento:
Article