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A flexible quasi-likelihood model for microbiome abundance count data.
Shi, Yiming; Li, Huilin; Wang, Chan; Chen, Jun; Jiang, Hongmei; Shih, Ya-Chen T; Zhang, Haixiang; Song, Yizhe; Feng, Yang; Liu, Lei.
Affiliation
  • Shi Y; Division of Biostatistics, Washington University in St. Louis, St. Louis, Missouri, USA.
  • Li H; Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, New York, USA.
  • Wang C; Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, New York, USA.
  • Chen J; Division of Computational Biology, Mayo Clinic, Rochester, Minnesota, USA.
  • Jiang H; Department of Statistics, Northwestern University, Evanston, Illinois, USA.
  • Shih YT; Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
  • Zhang H; Center for Applied Mathematics, Tianjin University, Tianjin, China.
  • Song Y; Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, Missouri, USA.
  • Feng Y; Department of Biostatistics, College of Global Public Health, New York University, New York, New York, USA.
  • Liu L; Division of Biostatistics, Washington University in St. Louis, St. Louis, Missouri, USA.
Stat Med ; 42(25): 4632-4643, 2023 11 10.
Article in En | MEDLINE | ID: mdl-37607718
In this article, we present a flexible model for microbiome count data. We consider a quasi-likelihood framework, in which we do not make any assumptions on the distribution of the microbiome count except that its variance is an unknown but smooth function of the mean. By comparing our model to the negative binomial generalized linear model (GLM) and Poisson GLM in simulation studies, we show that our flexible quasi-likelihood method yields valid inferential results. Using a real microbiome study, we demonstrate the utility of our method by examining the relationship between adenomas and microbiota. We also provide an R package "fql" for the application of our method.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Models, Statistical / Microbiota Limits: Humans Language: En Journal: Stat Med Year: 2023 Document type: Article Affiliation country: United States Country of publication: United kingdom

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Models, Statistical / Microbiota Limits: Humans Language: En Journal: Stat Med Year: 2023 Document type: Article Affiliation country: United States Country of publication: United kingdom