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Using multivariate mixed-effects selection models for analyzing batch-processed proteomics data with non-ignorable missingness.
Wang, Jiebiao; Wang, Pei; Hedeker, Donald; Chen, Lin S.
Affiliation
  • Wang J; Department of Public Health Sciences, University of Chicago, 5841 S. Maryland Ave., Chicago, IL, USA.
  • Wang P; Department of Genetics and Genomics Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, 770 Lexington Avenue, New York, NY, USA.
  • Hedeker D; Department of Public Health Sciences, University of Chicago, 5841 S. Maryland Ave., Chicago, IL, USA.
  • Chen LS; Department of Public Health Sciences, University of Chicago, 5841 S. Maryland Ave., Chicago, IL, USA.
Biostatistics ; 20(4): 648-665, 2019 10 01.
Article in En | MEDLINE | ID: mdl-29939200
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Full text: 1 Database: MEDLINE Main subject: Algorithms / Biostatistics / Models, Statistical / Proteomics Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Biostatistics Year: 2019 Type: Article Affiliation country: United States
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Full text: 1 Database: MEDLINE Main subject: Algorithms / Biostatistics / Models, Statistical / Proteomics Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Biostatistics Year: 2019 Type: Article Affiliation country: United States