Statistical First Aid: Interpretation of Health Research Data.

Biometrics ◽  
1994 ◽  
Vol 50 (4) ◽  
pp. 1231
Author(s):  
D. Howel ◽  
R. P. Hirsch ◽  
R. K. Riegelman
Keyword(s):  
Health Research ◽  
First Aid ◽  
Research Data

scholarly journals Challenges arising when seeking broad consent for health research data sharing: a qualitative study of perspectives in Thailand

2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Phaik Yeong Cheah ◽  
Nattapat Jatupornpimol ◽  
Borimas Hanboonkunupakarn ◽  
Napat Khirikoekkong ◽  
Podjanee Jittamala ◽  
...  
Keyword(s):  
Qualitative Study ◽  
Data Sharing ◽  
Health Research ◽  
Research Data ◽  
Broad Consent

scholarly journals Facilitating Harmonized Data Quality Assessments. A Data Quality Framework for Observational Health Research Data Collections With Software Implementations in R

2020 ◽  
Author(s):  
Carsten Schmidt ◽  
Stephan Struckmann ◽  
Cornelia Enzenbach ◽  
Achim Reineke ◽  
Jürgen Stausberg ◽  
...  
Keyword(s):  
Data Quality ◽  
Quality Indicators ◽  
Health Research ◽  
Research Data ◽  
Formal Concept ◽  
Software Implementations ◽  
Quality Framework ◽  
Data Collections ◽  
Quality Assessments ◽  
Data Quality Indicators

Abstract 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.

scholarly journals Research Stakeholders’ Views on Benefits and Challenges for Public Health Research Data Sharing in Kenya: The Importance of Trust and Social Relations

PLoS ONE ◽  
2015 ◽  
Vol 10 (9) ◽  
pp. e0135545 ◽  
Author(s):  
Irene Jao ◽  
Francis Kombe ◽  
Salim Mwalukore ◽  
Susan Bull ◽  
Michael Parker ◽  
...  
Keyword(s):  
Public Health ◽  
Data Sharing ◽  
Social Relations ◽  
Health Research ◽  
Public Health Research ◽  
Research Data
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