Doubly robust methods for handling confounding by clusterBiostatistics - Tập 17 Số 2 - Trang 264-276 - 2016
Johan Zetterqvist, Stijn Vansteelandt, Yudi Pawitan, Arvid Sjölander
Abstract
In clustered designs such as family studies, the exposure-outcome association is usually confounded by both cluster-constant and cluster-varying confounders. The influence of cluster-constant confounders can be eliminated by studying the exposure-outcome association within (conditional on) clusters, but additional regression modeling is usua...... hiện toàn bộ
Zero-inflated generalized Dirichlet multinomial regression model for microbiome compositional data analysisBiostatistics - Tập 20 Số 4 - Trang 698-713 - 2019
Zheng-Zheng Tang, Guanhua Chen
SummaryThere is heightened interest in using high-throughput sequencing technologies to quantify abundances of microbial taxa and linking the abundance to human diseases and traits. Proper modeling of multivariate taxon counts is essential to the power of detecting this association. Existing models are limited in handling excessive zero observations in taxon counts...... hiện toàn bộ
Latent variable modeling for the microbiomeBiostatistics - Tập 20 Số 4 - Trang 599-614 - 2019
Kris Sankaran, Susan Holmes
SummaryThe human microbiome is a complex ecological system, and describing its structure and function under different environmental conditions is important from both basic scientific and medical perspectives. Viewed through a biostatistical lens, many microbiome analysis goals can be formulated as latent variable modeling problems. However, although probabilistic l...... hiện toàn bộ
Sparse inverse covariance estimation with the graphical lassoBiostatistics - Tập 9 Số 3 - Trang 432-441 - 2008
Jerome H. Friedman, Trevor Hastie, Robert Tibshirani
Abstract
We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm—the graphical lasso—that is remarkably fast: It solves a 1000-node problem (∼500000 parameters) in at most a minute and is 30–4000 times faster than...... hiện toàn bộ