SL Screens

2021-08-08

Investigating the use of screens in Super Learner ensembles

Drew King, Brian D Williamson, Ying Huang

2021-08-08

Investigating the use of screens in Super Learner ensembles

Drew King, Brian D Williamson, Ying Huang

Abstract

Clinical research trials often generate "big data"

• Examples: vaccine and cancer early detection research

• Large number of participants = good

• Large number of variables -> challenging Machine learning increasingly used in biomedical research

• Helpful with a large number of variables

• Can identify complex relationships with outcome of interest

• Issue: performance can vary across algorithms Potential solution: use an ensemble of algorithms

• Combine results across procedures

• Allows enhanced flexibility

• Issue: no formal guidelines for combining certain procedures Goal: establish guidelines around ensemble procedures Potential impact in vaccine, cancer research

• Methodology of this research will be available upon peer review*

Results

Lasso + SL not always a helpful combo; surprising result.

• SL with screens or SL with lasso and screens = safe choice
• Lasso by itself = unreliable in most cases
• Lasso + SL with no screens = unreliable in many cases

Code will be available on GitHub once the paper is published

• Open source under the MIT license

Next Steps

Train researchers to follow our guidelines + practices. Conduct further research including:

• Building a study to address why the lasso alone is not ideal for variable selection
• Testing other algorithms that process biomedical data.
• Building this experiment in other programming language such as C or Python for better performance

Following publication, results will be replicable by others with scientific computing tools.