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A Modified Adaptive Lasso for Identifying Interactions in the Cox Model with the Heredity Constraint.

ABSTRACT: In many biomedical studies, identifying effects of covariate interactions on survival is a major goal. Important examples are treatment-subgroup interactions in clinical trials, and gene-gene or gene-environment interactions in genomic studies. A common problem when implementing a variable selection algorithm in such settings is the requirement that the model must satisfy the strong heredity constraint, wherein an interaction may be included in the model only if the interaction's component variables are included as main effects. We propose a modified Lasso method for the Cox regression model that adaptively selects important single covariates and pairwise interactions while enforcing the strong heredity constraint. The proposed method is based on a modified log partial likelihood including two adaptively weighted penalties, one for main effects and one for interactions. A two-dimensional tuning parameter for the penalties is determined by generalized cross-validation. Asymptotic properties are established, including consistency and rate of convergence, and it is shown that the proposed selection procedure has oracle properties, given proper choice of regularization parameters. Simulations illustrate that the proposed method performs reliably across a range of different scenarios.

SUBMITTER: Wang L

PROVIDER: S-EPMC4111275 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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A Modified Adaptive Lasso for Identifying Interactions in the Cox Model with the Heredity Constraint.

Wang Lu L Shen Jincheng J Thall Peter F PF

Statistics & probability letters 20141001

In many biomedical studies, identifying effects of covariate interactions on survival is a major goal. Important examples are treatment-subgroup interactions in clinical trials, and gene-gene or gene-environment interactions in genomic studies. A common problem when implementing a variable selection algorithm in such settings is the requirement that the model must satisfy the strong heredity constraint, wherein an interaction may be included in the model only if the interaction's component varia ...[more]

PMID: 25071299

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Project description:BackgroundThe genetic etiology of complex diseases in human has been commonly viewed as a complex process involving both genetic and environmental factors functioning in a complicated manner. Quite often the interactions among genetic variants play major roles in determining the susceptibility of an individual to a particular disease. Statistical methods for modeling interactions underlying complex diseases between single genetic variants (e.g. single nucleotide polymorphisms or SNPs) have been extensively studied. Recently, haplotype-based analysis has gained its popularity among genetic association studies. When multiple sequence or haplotype interactions are involved in determining an individual's susceptibility to a disease, it presents daunting challenges in statistical modeling and testing of the interaction effects, largely due to the complicated higher order epistatic complexity.ResultsIn this article, we propose a new strategy in modeling haplotype-haplotype interactions under the penalized logistic regression framework with adaptive L1-penalty. We consider interactions of sequence variants between haplotype blocks. The adaptive L1-penalty allows simultaneous effect estimation and variable selection in a single model. We propose a new parameter estimation method which estimates and selects parameters by the modified Gauss-Seidel method nested within the EM algorithm. Simulation studies show that it has low false positive rate and reasonable power in detecting haplotype interactions. The method is applied to test haplotype interactions involved in mother and offspring genome in a small for gestational age (SGA) neonates data set, and significant interactions between different genomes are detected.ConclusionsAs demonstrated by the simulation studies and real data analysis, the approach developed provides an efficient tool for the modeling and testing of haplotype interactions. The implementation of the method in R codes can be freely downloaded from http://www.stt.msu.edu/~cui/software.html.

Dataset Information

A Modified Adaptive Lasso for Identifying Interactions in the Cox Model with the Heredity Constraint.

Publications

A Modified Adaptive Lasso for Identifying Interactions in the Cox Model with the Heredity Constraint.

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