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psHarmonize: Facilitating reproducible large-scale pre-statistical data harmonization and documentation in R.
Stephen, John J; Carolan, Padraig; Krefman, Amy E; Sedaghat, Sanaz; Mansolf, Maxwell; Allen, Norrina B; Scholtens, Denise M.
Afiliación
  • Stephen JJ; Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.
  • Carolan P; Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.
  • Krefman AE; Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.
  • Sedaghat S; Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN 55455, USA.
  • Mansolf M; Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.
  • Allen NB; Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.
  • Scholtens DM; Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.
Patterns (N Y) ; 5(8): 101003, 2024 Aug 09.
Article en En | MEDLINE | ID: mdl-39233692
ABSTRACT
Combining pertinent data from multiple studies can increase the robustness of epidemiological investigations. Effective "pre-statistical" data harmonization is paramount to the streamlined conduct of collective, multi-study analysis. Harmonizing data and documenting decisions about the transformations of variables to a common set of categorical values and measurement scales are time consuming and can be error prone, particularly for numerous studies with large quantities of variables. The psHarmonize R package facilitates harmonization by combining multiple datasets, applying data transformation functions, and creating long and wide harmonized datasets. The user provides transformation instructions in a "harmonization sheet" that includes dataset names, variable names, and coding instructions and centrally tracks all decisions. The package performs harmonization, generates error logs as necessary, and creates summary reports of harmonized data. psHarmonize is poised to serve as a central feature of data preparation for the joint analysis of multiple studies.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Patterns (N Y) Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Patterns (N Y) Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos