Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Ruofan Bie"'
Autor:
Kirsten Voorhies, Ruofan Bie, John E. Hokanson, Scott T. Weiss, Ann Chen Wu, Julian Hecker, Georg Hahn, Dawn L. Demeo, Edwin Silverman, Michael H. Cho, Christoph Lange, Sharon M. Lutz
Publikováno v:
PLoS ONE, Vol 17, Iss 5 (2022)
To increase power and minimize bias in statistical analyses, quantitative outcomes are often adjusted for precision and confounding variables using standard regression approaches. The outcome is modeled as a linear function of the precision variables
Externí odkaz:
https://doaj.org/article/b7e1c3d9f635442eaeb83bf570e0dcfd
Publikováno v:
Statistics in Medicine. 41:5220-5241
Ultrahigh and high dimensional data are common in regression analysis for various fields, such as omics data, finance, and biological engineering. In addition to the problem of dimension, the data might also be contaminated. There are two main types
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bd42d40bd6c64bc34f085db42692e17c
https://doi.org/10.1002/sim.9563
https://doi.org/10.1002/sim.9563
Summary Causally interpretable meta-analysis combines information from a collection of randomized controlled trials to estimate treatment effects in a target population in which experimentation may not be possible but from which covariate information
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fad14d8d6f05a7c45500ea3f90ea044d
Publikováno v:
Statistics in medicineREFERENCES. 40(24)
In correlated data settings, analysts typically choose between fitting conditional and marginal models, whose parameters come with distinct interpretations, and as such the choice between the two should be made on scientific grounds. For settings whe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d4784cd1a443f2e9d5d82f4e1d8a656b
https://pubmed.ncbi.nlm.nih.gov/34251697
https://pubmed.ncbi.nlm.nih.gov/34251697
Publikováno v:
Statistics in Medicine. 37:4386-4403
In the research on complex diseases, gene expression (GE) data have been extensively used for clustering samples. The clusters so generated can serve as the basis for disease subtype identification, risk stratification, and many other purposes. With
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e387f4f0c0e51b0b208692e67302b3e4
https://doi.org/10.1002/sim.7928
https://doi.org/10.1002/sim.7928
Publikováno v:
Statistics in medicine. 37(29)
In the research on complex diseases, gene expression (GE) data have been extensively used for clustering samples. The clusters so generated can serve as the basis for disease subtype identification, risk stratification, and many other purposes. With
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::39da425a766c6bd2b7b26bed9be93885
https://pubmed.ncbi.nlm.nih.gov/30094873
https://pubmed.ncbi.nlm.nih.gov/30094873