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Methods for correcting inference based on outcomes predicted by machine learning.

Siruo Wang1, Tyler H McCormick2,3, Jeffrey T Leek4

  • 1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205.

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|November 19, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces postprediction inference (postpi), a novel method to correct statistical inference when using machine learning predicted outcomes. The postpi approach improves accuracy in medical and public health predictions.

Keywords:
interpretabilitymachine learningpostprediction inferencestatistics

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Area of Science:

  • Biostatistics
  • Machine Learning
  • Computational Biology

Background:

  • Machine learning models are increasingly used for outcome prediction in medicine and public health.
  • Statistical inference often fails to account for the difference between observed and predicted outcomes, leading to potential biases.

Purpose of the Study:

  • To develop and validate a method for correcting statistical inference when using predicted outcomes from complex machine learning models.
  • To improve the accuracy of variance estimation and subsequent statistical analyses in postprediction scenarios.

Main Methods:

  • Developed a postprediction inference (postpi) approach that models the relationship between observed and predicted outcomes.
  • Utilized a training, testing, and validation set framework to train prediction models and correct inference.
  • Applied the method to diverse datasets, including gene expression and verbal autopsy data.

Main Results:

  • The postpi method effectively corrects bias in statistical inference using predicted outcomes.
  • Demonstrated improvements in variance estimation and overall inference accuracy.
  • Validated the approach's broad applicability across different biomedical fields.

Conclusions:

  • Postprediction inference (postpi) offers a robust solution for accurate statistical analysis with machine learning-predicted outcomes.
  • The method enhances reliability in medical and public health research by addressing biases inherent in using predicted data.
  • An open-source R package is available for implementing the postpi approach.