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Comparing regression modeling strategies for predicting hometime.

Jessalyn K Holodinsky1, Amy Y X Yu2,3, Moira K Kapral2,4,5

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Machine learning models generally outperformed traditional statistical methods for analyzing patient hometime after stroke. Generalized boosting and random forests showed promise, but no model fully captured the complex hometime distribution.

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Area of Science:

  • Health Services Research
  • Biostatistics
  • Data Science

Background:

  • Hometime, a patient-centered outcome, measures time spent in the community post-hospitalization.
  • Hometime analysis is challenging due to non-normal distribution, excess zeros, and bounded limits.
  • Optimal statistical methods for hometime are currently undetermined.

Purpose of the Study:

  • To compare the performance of statistical and machine learning models in predicting 90-day hometime in stroke patients.
  • To identify the most accurate and least biased models for hometime analysis using administrative data.

Main Methods:

  • Adult stroke patients from Ontario, Canada (2010-2017) were identified using administrative data.
  • Fifteen statistical and machine learning models were developed and validated on independent datasets.
  • Model performance was assessed by predictive accuracy (RMSE, MAE) and bias.

Main Results:

  • Machine learning models generally showed lower error (RMSE, MAE) than statistical models.
  • Some statistical models exhibited lower or equal bias compared to machine learning models.
  • Machine learning models better handled non-linear interactions and constrained predictions within observed hometime ranges.

Conclusions:

  • Machine learning methods, particularly generalized boosting machines and random forests, showed superior performance over traditional statistical approaches.
  • No single model perfectly captured the complex, non-normal hometime distribution.
  • Further research is needed to incorporate factors beyond administrative data for extreme hometime values.