Prior elicitation, variable selection and Bayesian computation for logistic regression models

1999 ◽  
Vol 61 (1) ◽  
pp. 223-242 ◽  
Author(s):  
M.-H. Chen ◽  
J. G. Ibrahim ◽  
C. Yiannoutsos
Keyword(s):  
Logistic Regression ◽  
Variable Selection ◽  
Regression Models ◽  
Bayesian Computation ◽  
Prior Elicitation ◽  
Logistic Regression Models

Variable Selection in Logistic Regression Models

2004 ◽  
Vol 33 (3) ◽  
pp. 787-805 ◽  
Author(s):  
Dietmar Zellner ◽  
Frieder Keller ◽  
Günter E. Zellner
Keyword(s):  
Logistic Regression ◽  
Variable Selection ◽  
Regression Models ◽  
Logistic Regression Models

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

2015 ◽  
Vol 143 (13) ◽  
pp. 2786-2794 ◽  
Author(s):  
W. GU ◽  
A. R. VIEIRA ◽  
R. M. HOEKSTRA ◽  
P. M. GRIFFIN ◽  
D. COLE
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Variable Selection ◽  
Regression Models ◽  
Case Control Study ◽  
Case Control ◽  
Logistic Regression Models ◽  
Population Attributable Fractions ◽  
Attributable Fractions ◽  
Control Study

SUMMARYTo 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 selection. Our results show that the forward and backward variable selection of logistic regression models were not consistent for parameter estimation, with different significant exposures identified. By contrast, the random forest model produced estimated PAFs of grouped exposures consistent in rank order with results obtained from outbreak data, with egg-related exposures having the highest estimated PAF (22·1%, 95% confidence interval 8·5–31·8). Random forest might be structurally more coherent and efficient than logistic regression models for attributing Salmonella illnesses to sources involving many causal pathways.

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