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Abstract:
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In longitudinal studies, regularized variable selection methods have been extensively developed while accommodating the intra-correlation among repeated measurements. Despite the success, they are limited especially in accommodating structured sparsity. For example, in cancer research, strong correlations generally exist among omics features such as gene expressions. Ignoring such a correlation while conducting variable selection in longitudinal studies results in false identification and biased estimation. In this study, we have proposed a network based variable selection method under repeatedly measured disease phenotype. The strong interconnections among the omics predictors have been efficiently accommodated while performing variable selection. The advantage of the proposed method has been demonstrated in extensive simulations and a repeated measurement study with high dimensional SNP data.
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