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Comparison of Imputation Methods for Categorical Real-World Prostate Cancer Data with Natural Order.

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Multiple Imputation by Chained Equations (MICE) with random forest (mice_rf) best handles missing cancer data. This imputation method improved accuracy compared to k nearest neighbour (knn) and logistic regression, especially with more missing data.

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Area of Science:

  • Oncology
  • Biostatistics
  • Data Science

Background:

  • Missing values (NA) are common in cancer research, impacting prediction models and analyses.
  • Ordered categorical data, like tumor grading and staging, requires specialized imputation methods.
  • Incomplete patient data can affect risk classification and survival time analysis.

Purpose of the Study:

  • To compare the performance of various imputation methods for handling missing data in cancer research.
  • To evaluate imputation techniques on a real-world prostate cancer dataset.
  • To identify the most accurate imputation method for ordered categorical cancer data.

Main Methods:

  • Compared mode imputation, k nearest neighbour (knn), logistic regression with proportional odds (mice_polr), and random forest (mice_rf) within Multiple Imputation by Chained Equations (MICE).
  • Utilized a prostate cancer dataset from the Cancer Registry of Rhineland-Palatinate, Germany.
  • Simulated missing completely at random (MCAR) data at 10%, 20%, 30%, and 50% levels.

Main Results:

  • Random forest imputation (mice_rf) significantly outperformed other methods across all missing data percentages.
  • K nearest neighbour (knn) showed moderate performance, while logistic regression (mice_polr) performed worst.
  • Imputation accuracy improved with fewer categories, even category distribution, or a dominant category.

Conclusions:

  • MICE with random forest is a highly effective imputation strategy for ordered categorical cancer data.
  • The choice of imputation method significantly impacts the accuracy of cancer data analysis.
  • Data characteristics, such as category distribution, influence imputation method performance.