Rmonize
Tools for Data Harmonization
Integrated tools to support rigorous and well documented data
harmonization based on Maelstrom Research guidelines. The package includes
functions to assess and prepare input elements, apply specified processing
rules to generate harmonized datasets, validate data processing and identify
processing errors, and document and summarize harmonized outputs. The
harmonization process is defined and structured by two key user-generated
documents: the DataSchema (specifying the list of harmonized variables to
generate across datasets) and the Data Processing Elements (specifying the
input elements and processing algorithms to generate harmonized variables
in DataSchema formats). The package was developed to address key challenges
of retrospective data harmonization in epidemiology (as described in
Fortier I and al. (2017)
Tools for Data
Harmonization
Harmonizing data (achieving or improving inferential equivalence of data collected by separate studies) can be required in epidemiological research but is methodologically and technically challenging. Data collected by separate studies are typically heterogenous, and decisions on if and how to process data must be made, executed accurately, and documented in a transparent manner. Rmonize is an R package developed by Maelstrom Research to address some of the key challenges in this process and facilitate streamlined, reusable, and well documented harmonization pipelines.
Get an overview of processing with Rmonize and links to resources available for each step on the Process page.
For a quick start to using the package, see the vignettes Install your working environment and a Simple example of data processing with Rmonize.
