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Using decision trees to understand structure in missing data.

Nicholas J Tierney1, Fiona A Harden2, Maurice J Harden3

  • 1Department of Statistical Science, Mathematical Sciences, Science & Engineering Faculty, Queensland University of Technology, Brisbane, Queensland, Australia ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), Brisbane, Queensland, Australia.

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Summary
This summary is machine-generated.

Decision trees, including classification and regression trees (CART) and boosted regression trees (BRT), effectively reveal patterns in missing data. These models help identify factors contributing to data gaps, aiding researchers in understanding data structure.

Keywords:
EPIDEMIOLOGYOCCUPATIONAL & INDUSTRIAL MEDICINEPUBLIC HEALTHSTATISTICS & RESEARCH METHODS

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Area of Science:

  • Occupational Health
  • Data Science
  • Statistical Modeling

Background:

  • Missing data is a common challenge in occupational health research.
  • Understanding the structure of missing data is crucial for accurate analysis.
  • Traditional methods may not fully capture complex missingness patterns.

Purpose of the Study:

  • To demonstrate the application of decision tree models for understanding missing data structures.
  • To evaluate the effectiveness of Classification and Regression Trees (CART) and Boosted Regression Trees (BRT) in identifying missingness patterns.
  • To explore the capability of these models in describing artificially introduced missingness.

Main Methods:

  • Utilized an occupational health dataset from 7915 employees across 3 industrial sites.
  • Applied CART and BRT models using 'rpart' and 'gbm' packages in R statistical software.
  • Conducted a simulation study to assess model performance with introduced missingness.

Main Results:

  • CART and BRT models successfully highlighted missingness structures related to data type, site, visit frequency, and extreme values.
  • CART models identified specific variables and values causing missing data.
  • Variable importance varied between structured and unstructured missingness.

Conclusions:

  • Both CART and BRT models are effective for describing structural missingness in datasets.
  • CART models are recommended for exploratory analysis and identifying predictors of missingness.
  • BRT models offer insights into how other variables influence missingness.