Direct effects testing: A two-stage procedure to test for effect size and variable importance for correlated binary predictors and a binary response

In applications such as medical statistics and genetics, we encounter situations where a large number of highly correlated predictors explain a response. For example, the response may be a disease indicator and the predictors may be treatment indicators or single nucleotide polymorphisms (SNPs). Con...

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Bibliographic Details
Published in:Statistics in medicine 2010-10, Vol.29 (24), p.2544-2556
Main Authors: Sperrin, M., Jaki, T.
Format: Article
Language:English
Subjects:
Age of Onset
Alcohol Drinking - epidemiology
Comorbidity
contingency table
Coronary Disease - epidemiology
Data Interpretation, Statistical
direct effect
Genome-Wide Association Study - methods
high dimensional
Humans
Lasso
Medical statistics
Models, Statistical
noncentral hypergeometric distribution
Obesity - epidemiology
Polymorphism
Regression Analysis
Risk Factors
Rural Health - statistics & numerical data
Simulation
Smoking - epidemiology
South Africa - epidemiology
sparsity
Statistical methods
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Summary:In applications such as medical statistics and genetics, we encounter situations where a large number of highly correlated predictors explain a response. For example, the response may be a disease indicator and the predictors may be treatment indicators or single nucleotide polymorphisms (SNPs). Constructing a good predictive model in such cases is well studied. Less well understood is how to recover the ‘true sparsity pattern’, that is finding which predictors have direct effects on the response, and indicating the statistical significance of the results. Restricting attention to binary predictors and response, we study the recovery of the true sparsity pattern using a two‐stage method that separates establishing the presence of effects from inferring their exact relationship with the predictors. Simulations and a real data application demonstrate that the method discriminates well between associations and direct effects. Comparisons with lasso‐based methods demonstrate favourable performance of the proposed method. Copyright © 2010 John Wiley & Sons, Ltd.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.4014