title: Risk estimation using probability machines.
abstract: BACKGROUND
  Logistic regression has been the de facto, and often the only, model used in the description and analysis of relationships between a binary outcome and observed features. It is widely used to obtain the conditional probabilities of the outcome given predictors, as well as predictor effect size estimates using conditional odds ratios.
  
  
  RESULTS
  We show how statistical learning machines for binary outcomes, provably consistent for the nonparametric regression problem, can be used to provide both consistent conditional probability estimation and conditional effect size estimates. Effect size estimates from learning machines leverage our understanding of counterfactual arguments central to the interpretation of such estimates. We show that, if the data generating model is logistic, we can recover accurate probability predictions and effect size estimates with nearly the same efficiency as a correct logistic model, both for main effects and interactions. We also propose a method using learning machines to scan for possible interaction effects quickly and efficiently. Simulations using random forest probability machines are presented.
  
  
  CONCLUSIONS
  The models we propose make no assumptions about the data structure, and capture the patterns in the data by just specifying the predictors involved and not any particular model structure. So they do not run the same risks of model mis-specification and the resultant estimation biases as a logistic model. This methodology, which we call a "risk machine", will share properties from the statistical machine that it is derived from.
authors.affiliation: Clinical 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.
authors.forename: Abhijit
authors.initials: A
authors.lastname: Dasgupta
authors.forename: Silke
authors.initials: S
authors.lastname: Szymczak
authors.forename: Jason H
authors.initials: JH
authors.lastname: Moore
authors.forename: Joan E
authors.initials: JE
authors.lastname: Bailey-Wilson
authors.forename: James D
authors.initials: JD
authors.lastname: Malley