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An R-Based Landscape Validation of a Competing Risk Model
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ipd: an R package for conducting inference on predicted data.

Stephen Salerno1, Jiacheng Miao2, Awan Afiaz1,3

  • 1Public Health Sciences, Biostatistics, Fred Hutchinson Cancer Center, Seattle, WA 98109, United States.

Bioinformatics (Oxford, England)
|February 3, 2025
PubMed
Summary
This summary is machine-generated.

Introducing ipd, an R package for downstream modeling with imputed data. It simplifies inference on predicted data using AI/ML, offering user-friendly functions for model inspection and analysis.

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Area of Science:

  • Statistical Software
  • Machine Learning Applications
  • Data Science

Background:

  • The ipd package is an open-source R software for downstream modeling.
  • It addresses challenges in handling outcome data imputed by AI/ML algorithms.
  • The package is available on CRAN and GitHub with comprehensive documentation.

Purpose of the Study:

  • To introduce the ipd R package for statistical modeling.
  • To provide a user-friendly tool for inference on data with AI/ML-imputed outcomes.
  • To demonstrate the basic usage and features of the ipd package.

Main Methods:

  • The ipd package implements recent methods for inference on predicted data.
  • It offers a single, user-friendly wrapper function named 'ipd'.
  • Custom methods (print, summary, tidy, glance, augment) are included for model inspection.

Main Results:

  • The ipd package facilitates downstream modeling with imputed outcome data.
  • It enables straightforward inference on AI/ML-predicted data.
  • The package simplifies model inspection through custom S3 methods.

Conclusions:

  • ipd is a valuable open-source R package for researchers and data scientists.
  • It enhances the ability to perform reliable statistical modeling with imputed data.
  • The package promotes reproducible research and efficient data analysis.