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Risk estimation using probability machines.

Abhijit Dasgupta1, Silke Szymczak, Jason H Moore

  • 1Clinical Trials and Outcomes Branch, National Institute of Arthritis, Musculoskeletal and Skin Diseases, National Institutes of Health, Room 4-1350, Bldg 10 CRC, 10 Center Drive, Bethesda, MD 20892-1468, USA. abhijit.dasgupta@nih.gov.

Biodata Mining
|March 4, 2014
PubMed
Summary
This summary is machine-generated.

Statistical learning machines offer a flexible alternative to logistic regression for binary outcomes. These "risk machines" provide accurate probability and effect size estimates without assuming data structure, reducing mis-specification bias.

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Area of Science:

  • Statistics
  • Machine Learning
  • Biostatistics

Background:

  • Logistic regression is the standard for binary outcome analysis, estimating conditional probabilities and odds ratios.
  • However, logistic models can be prone to mis-specification and estimation bias due to rigid structural assumptions.

Purpose of the Study:

  • To demonstrate the utility of statistical learning machines for binary outcome analysis.
  • To provide consistent estimation of conditional probabilities and effect sizes using machine learning.
  • To introduce a novel method for efficiently scanning interaction effects.

Main Methods:

  • Utilized provably consistent statistical learning machines for nonparametric regression with binary outcomes.
  • Leveraged counterfactual reasoning for interpretable effect size estimation from learning machines.
  • Employed random forest probability machines for simulations and interaction effect scanning.

Main Results:

  • Learning machines provide consistent conditional probability and effect size estimates, comparable to logistic models when data is logistic.
  • These methods accurately recover probabilities and effect sizes, including main effects and interactions.
  • Proposed learning machine approach efficiently identifies potential interaction effects.

Conclusions:

  • The proposed 'risk machine' methodology makes no assumptions about data structure, enhancing flexibility.
  • This approach mitigates risks of model mis-specification and estimation bias inherent in logistic regression.
  • Risk machines inherit desirable properties from their underlying statistical machine learning models.