Publications of the Topic Group "Initial Data Analysis"
1. This is the main paper that introduces the initial data analysis framework and explains concepts.
Huebner M, le Cessie S, Schmidt CO, Vach W . A contemporary conceptual framework for initial data analysis. Observational Studies 2018; 4: 171-192. https://doi.org/10.1353/obs.2018.0014
2. This is a look at reporting pratices and recommendations for more transparency.
Huebner M, Vach W, le Cessie S, Schmidt C, Lusa L. Hidden Analyses: a review of reporting practice and recommendations for more transparent reporting of initial data analyses. BMC Med Res Meth 2020; 20:61. https://doi.org/10.1186/s12874-020-00942-y
3. Our first attempt at explaining initial data analysis aimed at a clinical audience. This is superseded by the framework paper (1).
Huebner M, Vach W, le Cessie S. A systematic approach to initial data analysis is good research practice. J Thoracic Cardiovas S. 2016; 151(1): 25-27. Link
4. A short introduction of aims and activities of our topic group:
Schmidt CO, Vach W, le Cessie S, Huebner M. STRATOS: Introducing the Initial Data Analysis Topic Group (TG3). Biometric Bulletin 2018; 35 (2): 10-11. Link
Related Publications. Collaborations with TG3 members
This paper provides an extensive overview of data quality aspects on observational studies and provides R code to conduct data quality assessments.
Schmidt CO, Struckmann S, Enzenbach C, Reineke A, Stausberg J, Damerow S, Huebner M, Schmidt B, Sauerbrei W, Richter A. Facilitating harmonized data quality assessments. A data quality framework for observational health research data collections with software implementations in R. BMC Med Res Meth 2021; 21(1): 1-15. Link
An overview of an R package to provide data quality assessments
Richter A , Schmidt CO , Krüger M , and Struckmann S. dataquieR: assessment of data quality in epidemiological research. Journal of Open Source Software 2021; 6(61), 3093, https://doi.org/10.21105/joss.03093
Data management and data cleaning for a longitudinal survey design
Lusa L and Huebner M. Organizing and Analyzing Data from the SHARE Study with an Application to Age and Sex Differences in Depressive Symptoms. Int. J. Environ. Res. Public Health 2021, 18(18), 9684; https://doi.org/10.3390/ijerph18189684
This is the accompanying R code for the IJEPRH paper (item 3)
Lusa L and Huebner M. Repository with R vignettes to support data organization and analysis for the SHARE study. 2021. https://doi.org/10.17605/OSF.IO/KGTX6