KMS Of Academy of mathematics and systems sciences, CAS
| Variable selection for random effects two-part models | |
Han, Dongxiao1; Liu, Lei2; Su, Xiaogang3; Johnson, Bankole4; Sun, Liuquan1
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| 2019-09-01 | |
| Source Publication | STATISTICAL METHODS IN MEDICAL RESEARCH
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| ISSN | 0962-2802 |
| Volume | 28Issue:9Pages:2697-2709 |
| Abstract | Random effects two-part models have been applied to longitudinal studies for zero-inflated (or semi-continuous) data, characterized by a large portion of zero values and continuous non-zero (positive) values. Examples include monthly medical costs, daily alcohol drinks, relative abundance of microbiome, etc. With the advance of information technology for data collection and storage, the number of variables available to researchers can be rather large in such studies. To avoid curse of dimensionality and facilitate decision making, it is critically important to select covariates that are truly related to the outcome. However, owing to its intricate nature, there is not yet a satisfactory variable selection method available for such sophisticated models. In this paper, we seek a feasible way of conducting variable selection for random effects two-part models on the basis of the recently proposed "minimum information criterion" (MIC) method. We demonstrate that the MIC formulation leads to a reasonable formulation of sparse estimation, which can be conveniently solved with SAS Proc NLMIXED. The performance of our approach is evaluated through simulation, and an application to a longitudinal alcohol dependence study is provided. |
| Keyword | High dimensional mixed effects pharmacogenetics precision medicine tuning parameter variable selection |
| DOI | 10.1177/0962280218784712 |
| Language | 英语 |
| Funding Project | AHRQ[R01 HS 020263] ; National Natural Science Foundation of China[11771431] ; National Natural Science Foundation of China[11690015] ; Key Laboratory of RCSDS, CAS[2008DP173182] |
| WOS Research Area | Health Care Sciences & Services ; Mathematical & Computational Biology ; Medical Informatics ; Mathematics |
| WOS Subject | Health Care Sciences & Services ; Mathematical & Computational Biology ; Medical Informatics ; Statistics & Probability |
| WOS ID | WOS:000484532300010 |
| Publisher | SAGE PUBLICATIONS LTD |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/35523 |
| Collection | 应用数学研究所 |
| Corresponding Author | Liu, Lei |
| Affiliation | 1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China 2.Washington Univ, Div Biostat, St Louis, MO 63110 USA 3.Univ Texas El Paso, Dept Math Sci, El Paso, TX 79968 USA 4.Univ Maryland, Dept Psychiat, Baltimore, MD 21201 USA |
| Recommended Citation GB/T 7714 | Han, Dongxiao,Liu, Lei,Su, Xiaogang,et al. Variable selection for random effects two-part models[J]. STATISTICAL METHODS IN MEDICAL RESEARCH,2019,28(9):2697-2709. |
| APA | Han, Dongxiao,Liu, Lei,Su, Xiaogang,Johnson, Bankole,&Sun, Liuquan.(2019).Variable selection for random effects two-part models.STATISTICAL METHODS IN MEDICAL RESEARCH,28(9),2697-2709. |
| MLA | Han, Dongxiao,et al."Variable selection for random effects two-part models".STATISTICAL METHODS IN MEDICAL RESEARCH 28.9(2019):2697-2709. |
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