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Optimizing large real-world data analysis with parquet files in R: A step-by-step tutorial.

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Summary

This study presents an efficient R-based method for analyzing big real-world evidence (RWE) data, reducing storage needs and improving analysis speed. This approach supports open science practices in RWE research.

Keywords:
Rbig datacohort buildingopen sciencepharmacoepidemiologyreal-world data

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Area of Science:

  • Health Informatics
  • Data Science
  • Pharmacovigilance

Background:

  • Real-world evidence (RWE) studies often involve large datasets, posing challenges for open-source programming languages.
  • Facilitating open science practices in RWE research requires efficient data handling methods.

Purpose of the Study:

  • To demonstrate an efficient approach for RWE researchers to utilize the R programming language for big data analysis.
  • To enable RWE analysis tasks, from cohort building to final reporting, using open-source tools.

Main Methods:

  • Developed an R function to transform Merative Marketscan data (2017-2019) into parquet format for R compatibility.
  • Compared data size, numerical consistency, and exploratory task runtimes between transformed and original data (SAS).
  • Conducted a simplified replication of a literature study to showcase practical application.

Main Results:

  • Transformed data size was 10%-43% of original files, indicating high storage efficiency.
  • R exploration tasks on transformed data generally outperformed original data runtimes using SAS.
  • Demonstrated efficient implementation of an RWE study using the converted data.

Conclusions:

  • A free and efficient solution is presented to enable open-source programming languages with large real-world databases.
  • This approach facilitates the adoption of open science practices in real-world evidence research.