See the published version of this preprint at BMC Medical Research Methodology.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
Research Article
Carsten Schmidt
Greifswald University Hospital
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background No standards exist for the handling and reporting of data quality in health research. This work introduces a data quality framework for observational health research data collections with supporting software implementations to facilitate harmonized data quality assessments.
Methods Developments were guided by the evaluation of an existing data quality framework and literature reviews. Functions for the computation of data quality indicators were written in R. The concept and implementations are illustrated based on data from the population-based Study of Health in Pomerania (SHIP).
Results The data quality framework comprises 34 data quality indicators. These target three aspects of data quality: compliance with pre-specified structural and technical requirements (Integrity), presence of data values (completeness), and error in the data values (correctness). R functions calculate data quality metrics based on the provided study data and metadata and R Markdown reports are generated. Guidance on the concept and tools is available through a dedicated website.
Conclusions The presented data quality framework is the first of its kind for observational health research data collections that links a formal concept to implementations in R. The framework and tools facilitate harmonized data quality assessments in pursue of transparent and reproducible research. Application scenarios comprise data quality monitoring while a study is carried out as well as performing an initial data analysis before starting substantive scientific analyses.
Keywords
Data Quality, Observational Health Studies, Data Quality Indicators, Data Quality Monitoring, Initial Data Analysis, R
Figure 1
Figure 1
Figure 1
Figure 2
Figure 2
Figure 2
Figure 3
Figure 3
Figure 3
Loading...
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Medical Research Methodology.
Version 1
Posted 10 Dec, 2020
No community comments so far
Editorial decision: Major revision
On 10 Feb, 2021
Reviews received
Received 09 Feb, 2021
Reviews received
Received 20 Jan, 2021
Reviewers agreed
On 19 Jan, 2021
Reviewers agreed
On 03 Jan, 2021
Reviewers invited
Invitations sent on 13 Dec, 2020
Editor assigned
On 08 Dec, 2020
Editor invited
On 08 Dec, 2020
Submission checks complete
On 08 Dec, 2020
First submitted
On 01 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Health Economics & Outcomes Research
Learn more about our company.
A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
See the published version of this preprint at BMC Medical Research Methodology.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Carsten Schmidt
Greifswald University Hospital
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Medical Research Methodology.
Version 1
Posted 10 Dec, 2020
No community comments so far
Editorial decision: Major revision
On 10 Feb, 2021
Reviews received
Received 09 Feb, 2021
Reviews received
Received 20 Jan, 2021
Reviewers agreed
On 19 Jan, 2021
Reviewers agreed
On 03 Jan, 2021
Reviewers invited
Invitations sent on 13 Dec, 2020
Editor assigned
On 08 Dec, 2020
Editor invited
On 08 Dec, 2020
Submission checks complete
On 08 Dec, 2020
First submitted
On 01 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Health Economics & Outcomes Research
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Medical Research Methodology.
Background No standards exist for the handling and reporting of data quality in health research. This work introduces a data quality framework for observational health research data collections with supporting software implementations to facilitate harmonized data quality assessments.
Methods Developments were guided by the evaluation of an existing data quality framework and literature reviews. Functions for the computation of data quality indicators were written in R. The concept and implementations are illustrated based on data from the population-based Study of Health in Pomerania (SHIP).
Results The data quality framework comprises 34 data quality indicators. These target three aspects of data quality: compliance with pre-specified structural and technical requirements (Integrity), presence of data values (completeness), and error in the data values (correctness). R functions calculate data quality metrics based on the provided study data and metadata and R Markdown reports are generated. Guidance on the concept and tools is available through a dedicated website.
Conclusions The presented data quality framework is the first of its kind for observational health research data collections that links a formal concept to implementations in R. The framework and tools facilitate harmonized data quality assessments in pursue of transparent and reproducible research. Application scenarios comprise data quality monitoring while a study is carried out as well as performing an initial data analysis before starting substantive scientific analyses.
Figure 1
Figure 1
Figure 1
Figure 2
Figure 2
Figure 2
Figure 3
Figure 3
Figure 3
Loading...