MiRKAT-S: a community-level test of association between the microbiota and survival times.
Community-level analysis of the human microbiota has culminated in the discovery of relationships between overall shifts in the microbiota and a wide range of diseases and conditions. However, existing work has primarily focused on analysis of relatively simple dichotomous or quantitative outcomes, for example, disease status or biomarker levels. Recently, there is also considerable interest in the relationship between the microbiota and censored survival outcomes, such as in clinical trials. How to conduct community-level analysis with censored survival outcomes is unclear, since standard dissimilarity-based tests cannot accommodate censored survival times and no alternative methods exist.



We develop a new approach, MiRKAT-S, for community-level analysis of microbiome data with censored survival times. MiRKAT-S uses ecologically informative distance metrics, such as the UniFrac distances, to generate matrices of pairwise distances between individuals' taxonomic profiles. The distance matrices are transformed into kernel (similarity) matrices, which are used to compare similarity in the microbiota to similarity in survival times between individuals.



Simulation studies using synthetic microbial communities demonstrate correct control of type I error and adequate power. We also apply MiRKAT-S to examine the relationship between the gut microbiota and survival after allogeneic blood or bone marrow transplant.



We present MiRKAT-S, a method that facilitates community-level analysis of the association between the microbiota and survival outcomes and therefore provides a new approach to analysis of microbiome data arising from clinical trials.
Department of Biostatistics, University of Washington, 1705 NE Pacific Street, Seattle, Washington, USA.
Anna
A
Plantinga
Public Health Sciences Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, Washington, USA.
Xiang
X
Zhan
Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, 615 N Wolfe St, Baltimore, Maryland, USA.
Ni
N
Zhao
Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, Minnesota, USA.
Jun
J
Chen
Departments of Genomic Medicine and Stem Cell Transplantation, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, Unit 1954, TX, USA.
Robert R
RR
Jenq
Department of Biostatistics, University of Washington, 1705 NE Pacific Street, Seattle, Washington, USA. mcwu@fredhutch.org.|Public Health Sciences Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, Washington, USA. mcwu@fredhutch.org.
Michael C
MC
Wu
D001803:Blood Transfusion / Q000401:mortality*; D016026:Bone Marrow Transplantation / Q000401:mortality*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D000069196:Gastrointestinal Microbiome / Q000235:genetics*; D041981:Gastrointestinal Tract / Q000382:microbiology*; D006086:Graft vs Host Disease / Q000401:mortality*; D006801:Humans; D008962:Models, Theoretical; D012336:RNA, Ribosomal, 16S / Q000235:genetics; D016019:Survival Analysis; D065227:Transfusion Reaction / Q000401:mortality*; D016896:Treatment Outcome
D012336:RNA, Ribosomal, 16S
Community-level analysis; Distance-based analysis; Kernel machine regression; Survival data