Super Learner Prediction and Variable Importance in Nursing Home Resident Suicidal Ideation

Oregon Convention Center 

The super learner method combines the stacking algorithm and regression analysis to obtain weighted predictions from varied statistical strategies for model prediction. It is shown to perform no worse than any single prediction method as well as to provide consistent estimates. The targeted maximum likelihood estimation (TMLE) method was further introduced for variable importance analyses, in which super learner predictions were compared between the saturated model and reduced models when each variable was left out. Variable importance was profiled by corresponding p-values.

In the study of nursing home resident suicide ideation, we first performed individual modeling for each of the eleven parametric or non-parametric strategies. Cross-validation was implemented in each strategy, and the aggregated estimates for each algorithm were approached. We further estimated the composite parameter estimates by enameling all model specific estimates, in which mean squared error (MSE) was used to identify best weights for the assembling. The TMLE method was used to identify ten most important risk factors associated with nursing home resident suicide ideation.

Super learner

targeted maximum likelihood

risk analysis 

Health Policy Statistics Section