Variable Selection via Fused Sparse‐Group Lasso Penalized Multi‐state Models Incorporating Molecular Data
Biometrical Journal, vol. 67, pp. e70087
Abstract
In multi-state models based on high-dimensional data, effective modeling strategies are required to determine an optimal, ideally parsimonious model. In particular, linking covariate effects across transitions is needed to conduct joint variable selection. A useful technique to reduce model complexity is to address homogeneous covariate effects for distinct transitions. We integrate this approach to data-driven variable selection by extended regularization methods within multi-state model building. We propose the fused sparse-group lasso (FSGL) penalized Cox-type regression in the framework of multi-state models combining the penalization concepts of pairwise differences of covariate effects along with transition-wise grouping. For optimization, we adapt the alternating direction method of multipliers (ADMM) algorithm to transition-specific hazards regression in the multi-state setting. In a simulation study and application to acute myeloid leukemia (AML) data, we evaluate the algorithm's ability to select a sparse model incorporating relevant transition-specific effects and similar cross-transition effects. We investigate settings in which the combined penalty is beneficial compared to global lasso regularization. Clinical Trial Registration: The AMLSG 09-09 trial is registered with ClinicalTrials.gov (NCT00893399) and has been completed.
Authors 5
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Kaya Miah corresponding
German Cancer Research Center · Heidelberg University
Affiliation as printed
Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany
Medical Faculty Heidelberg University Heidelberg Germany
Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
Medical Faculty, Heidelberg University, Heidelberg, Germany
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Jelle J. Goeman Aachen Department of Biomedical Data Sciences Leiden University Medical Center Leiden The Netherlands Department of Biomedical Data Sciences
Leiden University Medical Center
Affiliation as printed
Department of Biomedical Data Sciences Leiden University Medical Center (LUMC) Leiden The Netherlands
Department of Biomedical Data Sciences, Leiden University Medical Center (LUMC), Leiden, The Netherlands
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Hein Putter Aachen Department of Biomedical Data Sciences Leiden University Medical Center Leiden The Netherlands Department of Biomedical Data Sciences Mathematical Institute
Leiden University · Leiden University Medical Center
Affiliation as printed
Department of Biomedical Data Sciences Leiden University Medical Center (LUMC) Leiden The Netherlands
Mathematical Institute Leiden University Leiden The Netherlands
Department of Biomedical Data Sciences, Leiden University Medical Center (LUMC), Leiden, The Netherlands
Mathematical Institute, Leiden University, Leiden, The Netherlands
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Affiliation as printed
Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany
Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
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Affiliation as printed
Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany
Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
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