Penalized estimation of a class of single‐index varying‐coefficient models for integrative genomic analysis
Recent technological advances have made it possible to collect high‐dimensional genomic data along with clinical data on a large number of subjects. In the studies of chronic diseases such as cancer, it is of great interest to integrate clinical and genomic data to build a comprehensive understandin...
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| Published in | Biometrical journal Vol. 65; no. 1; pp. e2100139 - n/a |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Germany
Wiley - VCH Verlag GmbH & Co. KGaA
01.01.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0323-3847 1521-4036 1521-4036 |
| DOI | 10.1002/bimj.202100139 |
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| Abstract | Recent technological advances have made it possible to collect high‐dimensional genomic data along with clinical data on a large number of subjects. In the studies of chronic diseases such as cancer, it is of great interest to integrate clinical and genomic data to build a comprehensive understanding of the disease mechanisms. Despite extensive studies on integrative analysis, it remains an ongoing challenge to model the interaction effects between clinical and genomic variables, due to high dimensionality of the data and heterogeneity across data types. In this paper, we propose an integrative approach that models interaction effects using a single‐index varying‐coefficient model, where the effects of genomic features can be modified by clinical variables. We propose a penalized approach for separate selection of main and interaction effects. Notably, the proposed methods can be applied to right‐censored survival outcomes based on a Cox proportional hazards model. We demonstrate the advantages of the proposed methods through extensive simulation studies and provide applications to a motivating cancer genomic study. |
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| AbstractList | Recent technological advances have made it possible to collect high‐dimensional genomic data along with clinical data on a large number of subjects. In the studies of chronic diseases such as cancer, it is of great interest to integrate clinical and genomic data to build a comprehensive understanding of the disease mechanisms. Despite extensive studies on integrative analysis, it remains an ongoing challenge to model the interaction effects between clinical and genomic variables, due to high dimensionality of the data and heterogeneity across data types. In this paper, we propose an integrative approach that models interaction effects using a single‐index varying‐coefficient model, where the effects of genomic features can be modified by clinical variables. We propose a penalized approach for separate selection of main and interaction effects. Notably, the proposed methods can be applied to right‐censored survival outcomes based on a Cox proportional hazards model. We demonstrate the advantages of the proposed methods through extensive simulation studies and provide applications to a motivating cancer genomic study. Recent technological advances have made it possible to collect high-dimensional genomic data along with clinical data on a large number of subjects. In the studies of chronic diseases such as cancer, it is of great interest to integrate clinical and genomic data to build a comprehensive understanding of the disease mechanisms. Despite extensive studies on integrative analysis, it remains an ongoing challenge to model the interaction effects between clinical and genomic variables, due to high dimensionality of the data and heterogeneity across data types. In this paper, we propose an integrative approach that models interaction effects using a single-index varying-coefficient model, where the effects of genomic features can be modified by clinical variables. We propose a penalized approach for separate selection of main and interaction effects. Notably, the proposed methods can be applied to right-censored survival outcomes based on a Cox proportional hazards model. We demonstrate the advantages of the proposed methods through extensive simulation studies and provide applications to a motivating cancer genomic study.Recent technological advances have made it possible to collect high-dimensional genomic data along with clinical data on a large number of subjects. In the studies of chronic diseases such as cancer, it is of great interest to integrate clinical and genomic data to build a comprehensive understanding of the disease mechanisms. Despite extensive studies on integrative analysis, it remains an ongoing challenge to model the interaction effects between clinical and genomic variables, due to high dimensionality of the data and heterogeneity across data types. In this paper, we propose an integrative approach that models interaction effects using a single-index varying-coefficient model, where the effects of genomic features can be modified by clinical variables. We propose a penalized approach for separate selection of main and interaction effects. Notably, the proposed methods can be applied to right-censored survival outcomes based on a Cox proportional hazards model. We demonstrate the advantages of the proposed methods through extensive simulation studies and provide applications to a motivating cancer genomic study. Recent technological advances have made it possible to collect high‐dimensional genomic data along with clinical data on a large number of subjects. In the studies of chronic diseases such as cancer, it is of great interest to integrate clinical and genomic data to build a comprehensive understanding of the disease mechanisms. Despite extensive studies on integrative analysis, it remains an ongoing challenge to model the interaction effects between clinical and genomic variables, due to high dimensionality of the data and heterogeneity across data types. In this paper, we propose an integrative approach that models interaction effects using a single‐index varying‐coefficient model, where the effects of genomic features can be modified by clinical variables. We propose a penalized approach for separate selection of main and interaction effects. Notably, the proposed methods can be applied to right‐censored survival outcomes based on a Cox proportional hazards model. We demonstrate the advantages of the proposed methods through extensive simulation studies and provide applications to a motivating cancer genomic study. |
| Author | Jiang, Binyan Ng, Hoi Min Wong, Kin Yau |
| Author_xml | – sequence: 1 givenname: Hoi Min orcidid: 0000-0002-1470-9251 surname: Ng fullname: Ng, Hoi Min organization: The Hong Kong Polytechnic University – sequence: 2 givenname: Binyan surname: Jiang fullname: Jiang, Binyan organization: The Hong Kong Polytechnic University – sequence: 3 givenname: Kin Yau orcidid: 0000-0001-9066-1619 surname: Wong fullname: Wong, Kin Yau email: kin-yau.wong@polyu.edu.hk organization: The Hong Kong Polytechnic University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35837982$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | adaptive lasso Cancer Chronic illnesses Computer Simulation Genomic analysis Genomics group penalty Heterogeneity Humans interaction Neoplasms - genetics Proportional Hazards Models semiparametric models splines Statistical models |
| Title | Penalized estimation of a class of single‐index varying‐coefficient models for integrative genomic analysis |
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