Background
Combining and co-analyzing data from different studies offers potential advantages for addressing research questions, but data items collected by different studies must first be made suitably equivalent, i.e., harmonized. This process of data harmonization is essential but challenging to implement in a rigorous and transparent way. To help address these challenges, Maelstrom Research developed guidelines for rigorous retrospective data harmonization. An overview of the iterative steps of this process are shown in Figure 1.
harmonizR
The harmonizR package addresses practical aspects of data harmonization processing (guidelines Step 3) and facilitates evaluation (Step 4) and documentation (Step 5) of harmonization products. It was developed to meet certain needs of Maelstrom’s collaborative harmonization initiatives, including integration with the OBiBa software, but is intended as a general resource for diverse harmonization efforts.
Get started
Install the package
# To install the R package:
install.packages('harmonizR')
library(harmonizR)
#if you need help with the package, please use:
harmonizR_help()