The use and misuse of surrogate variables in environmental epidemiology

Frederick W. Lipfert

doi:10.1002/jem.40

The use and misuse of surrogate variables in environmental epidemiology

Journal of Environmental Medicine ◽

10.1002/jem.40 ◽

1999 ◽

Vol 1 (4) ◽

pp. 267-278 ◽

Cited By ~ 1

Author(s):

Frederick W. Lipfert

Keyword(s):

Environmental Epidemiology ◽

Surrogate Variables

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Journal of Exposure Science & Environmental Epidemiology

10.1038/41370.1559-064x ◽

2019 ◽

Keyword(s):

Environmental Epidemiology ◽

Exposure Science

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Faculty Opinions recommendation of Marginal structural models to control for time-varying confounding in occupational and environmental epidemiology.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.718095221.793482998 ◽

2013 ◽

Author(s):

Sherwood Burge

Keyword(s):

Structural Models ◽

Environmental Epidemiology ◽

Marginal Structural Models ◽

Time Varying

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Response to “Comment on ‘Optimal Exposure Biomarkers for Nonpersistent Chemicals in Environmental Epidemiology’”

Environmental Health Perspectives ◽

10.1289/ehp.1611282 ◽

2016 ◽

Vol 124 (4) ◽

Cited By ~ 2

Author(s):

Antonia M. Calafat ◽

Matthew P. Longnecker ◽

Holger M. Koch ◽

Shanna H. Swan ◽

Russ Hauser ◽

...

Keyword(s):

Environmental Epidemiology ◽

Exposure Biomarkers

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Surrogate minimal depth as an importance measure for variables in random forests

Bioinformatics ◽

10.1093/bioinformatics/btz149 ◽

2019 ◽

Vol 35 (19) ◽

pp. 3663-3671 ◽

Cited By ~ 6

Author(s):

Stephan Seifert ◽

Sven Gundlach ◽

Silke Szymczak

Keyword(s):

Variable Selection ◽

Supplementary Information ◽

Predictor Variables ◽

Importance Measure ◽

Surrogate Variables ◽

Machine Learning Approach ◽

Complex Relationships ◽

Minimal Depth ◽

Insight Into ◽

Causal Variables

Abstract Motivation It has been shown that the machine learning approach random forest can be successfully applied to omics data, such as gene expression data, for classification or regression and to select variables that are important for prediction. However, the complex relationships between predictor variables, in particular between causal predictor variables, make the interpretation of currently applied variable selection techniques difficult. Results Here we propose a new variable selection approach called surrogate minimal depth (SMD) that incorporates surrogate variables into the concept of minimal depth (MD) variable importance. Applying SMD, we show that simulated correlation patterns can be reconstructed and that the increased consideration of variable relationships improves variable selection. When compared with existing state-of-the-art methods and MD, SMD has higher empirical power to identify causal variables while the resulting variable lists are equally stable. In conclusion, SMD is a promising approach to get more insight into the complex interplay of predictor variables and outcome in a high-dimensional data setting. Availability and implementation https://github.com/StephanSeifert/SurrogateMinimalDepth. Supplementary information Supplementary data are available at Bioinformatics online.

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