a general framework for formal tests of interaction after exhaustive search methods with applications to mdr and mdr-pdt一般正式的测试框架的交互后耐多药和mdr-pdt穷举有哪些信誉好的足球投注网站方法与应用程序.pdfVIP

A General Framework for Formal Tests of Interaction after Exhaustive Search Methods with Applications to MDR and MDR-PDT 1,2,3 2 2 2 3 Todd L. Edwards , Stephen D. Turner , Eric S. Torstenson , Scott M. Dudek , Eden R. Martin , Marylyn D. Ritchie2* 1 Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America, 2 Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America, 3 Center for Genetic Epidemiology and Statistical Genetics, John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, Florida, United States of America Abstract The initial presentation of multifactor dimensionality reduction (MDR) featured cross-validation to mitigate over-fitting, computationally efficient searches of the epistatic model space, and variable construction with constructive induction to alleviate the curse of dimensionality. However, the method was unable to differentiate association signals arising from true interactions from those due to independent main effects at individual loci. This issue leads to problems in inference and interpretability for the results from MDR and the family-based compliment the MDR-pedigree disequilibrium test (PDT). A suggestion from previous work was to fit regression models post hoc to specifically evaluate the null hypothesis of no interaction for MDR or MDR-PDT models. We demonstrate with simulation that fitting a regression model on the same data as that analyzed by MDR or MDR-PDT is not a valid test of interaction. This is likely to be true for any other procedure that searches for model