Use of random forest to estimate population attributable fractions from a case-control study of Salmonella enterica serotype Enteritidis infections.

ABSTRACT: To design effective food safety programmes we need to estimate how many sporadic foodborne illnesses are caused by specific food sources based on case-control studies. Logistic regression has substantive limitations for analysing structured questionnaire data with numerous exposures and missing values. We adapted random forest to analyse data of a case-control study of Salmonella enterica serotype Enteritidis illness for source attribution. For estimation of summary population attributable fractions (PAFs) of exposures grouped into transmission routes, we devised a counterfactual estimator to predict reductions in illness associated with removing grouped exposures. For the purpose of comparison, we fitted the data using logistic regression models with stepwise forward and backward variable

SUBMITTER: Gu W 

PROVIDER: S-EPMC9151037 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature