Скачать или смотреть Retrospective clinical data harmonisation reporting using R and Quarto - Jeremy Selva

There has been an increase of projects that involved data pooling from multiple sources. This is because combining data is an economical way to increase the statistical power of an analysis of a rare outcome that could not be addressed using data from a single project. Prior to statistical or machine learning analysis, a data steward must be able to sort through these heterogeneous inputs and document this process in a coherent way for different stakeholders. Despite its importance in the big data environment, there are limit resources on how to document this process in a structured, efficient and robust way. This presentation will provide an overview on how I create clinical data harmonization reports using some R packages and a Quarto book project. A small preview can be found in https://github.com/JauntyJJS/harmonis...

The audience in this talk will be able to know the basic framework of creating a Quarto Book or website to document data harmonisation processes, the basic workflow during the data harmonisation process, how to do data validation when writing code for data harmonisation to ensure code workflow is robust to changes in the input data, ways to show to higher management (with limited programming experience) in the harmonisation report that your code works (It is not enough to say that I use test units), able to write an R script to create many data harmonisation reports (One technical report for each cohort pooled and one report that summarised the data harmonisation process for all cohorts).