Results 51 to 60 of about 3,822,020 (321)

lmerSeq: an R package for analyzing transformed RNA-Seq data with linear mixed effects models

BMC Bioinformatics, 2022
Background Studies that utilize RNA Sequencing (RNA-Seq) in conjunction with designs that introduce dependence between observations (e.g. longitudinal sampling) require specialized analysis tools to accommodate this additional complexity.
Brian E. Vestal, Elizabeth Wynn, Camille M. Moore   +2 more
doaj   +1 more source

Prognostic Impact of Treatment Modalities, Including Targeted Compartmental Radio‐Immunotherapy, in a Cohort of Neuroblastoma Patients With CNS Metastases at Relapse

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Introduction Neuroblastoma (NB) with central nervous system (CNS) metastases is rare at diagnosis, but occurs more often during relapse/progression. Patients with CNS metastases face a dismal prognosis, with no standardized curative treatment available.
Vicente Santa‐Maria Lopez, Anna Felip‐Badia, Marina Caballero‐Bellon, Nazaret Sanchez‐Sierra, Alicia Castañeda, Moira Garraus, Maite Gorostegui, Margarida Simão‐Rafael, Juan Pablo Muñoz, Marta Perez‐Somarriba, Salvador Mañe, Mariana Cecilia Planells, Sara Perez‐Jaume, Jaume Mora   +13 more
wiley   +1 more source

mplot: An R Package for Graphical Model Stability and Variable Selection Procedures

Journal of Statistical Software, 2018
The mplot package provides an easy to use implementation of model stability and variable inclusion plots (Müller and Welsh 2010; Murray, Heritier, and Müller 2013) as well as the adaptive fence (Jiang, Rao, Gu, and Nguyen 2008; Jiang, Nguyen, and Rao ...
Garth Tarr, Samuel Müller, Alan H. Welsh   +2 more
doaj   +1 more source

Exploring efficient linear mixed models to detect quantitative trait locus-by-environment interactions

G3: Genes, Genomes, Genetics, 2021
Genotype-by-environment (G × E) interactions are important for understanding genotype–phenotype relationships. To date, various statistical models have been proposed to account for G × E effects, especially in genomic selection (GS) studies.
Eiji Yamamoto, Hiroshi Matsunaga
doaj   +1 more source

A Note on the Identifiability of Generalized Linear Mixed Models [PDF]

, 2014
I present here a simple proof that, under general regularity conditions, the standard parametrization of generalized linear mixed model is identifiable.
Labouriau, Rodrigo
core  

Patient‐Level Barriers and Facilitators to Inpatient Physical Therapy in Adolescents and Young Adults With a Hematological Malignancy: A Qualitative Study

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Despite their increased risk for functional impairment resulting from cancer and its treatments, few adolescents and young adults (AYAs) with a hematological malignancy receive the recommended or therapeutic dose of exercise per week during inpatient hospitalizations.
Jennifer A. Kelleher, Jennifer L. Bernstein, Katia M. Crisler, Julia K. Herriott, Kimberly L. Klages, Robin E. Norris, Mariann L. Strenk, Sarah E. Bills, Meghan E. McGrady   +8 more
wiley   +1 more source

Multivariate Generalized Linear Mixed Models for Count Data

Austrian Journal of Statistics
Univariate regression models have rich literature for counting data. However, this is not the case for multivariate count data. Therefore, we present the Multivariate Generalized Linear Mixed Models framework that deals with a multivariate set of ...
Guilherme Parreira da Silva, Henrique Aparecido Laureano, Ricardo Rasmussen Petterle, Paulo Justiniano Ribeiro Júnior, Wagner Hugo Bonat   +4 more
doaj   +1 more source

Non-linear Mixed Models in a Dose Response Modelling

InPrime, 2019
Study designs in which an outcome is measured more than once from time to time result in longitudinal data. Most of the methodological works have been done in the setting of linear and generalized linear models, where some amount of linearity is retained.
Madona Yunita Wijaya
doaj   +1 more source

D-optimal designs formulti-response linear mixed models [PDF]

, 2018
Linear mixed models have become popular in many statistical applications duringrecent years. However design issues for multi-response linear mixed models are rarelydiscussed.
Liu, Xin, Wong, Weng Kee, Yue, Rong-Xian
core  

Clustering in linear mixed models with Dirichlet process mixtures using EM algorithm [PDF]

, 2011
In linear mixed models the assumption of normally distributed random effects is often inappropriate and unnecessary restrictive. The proposed Dirichlet process mixture assumes a hierarchical Gaussian mixture.
Heinzl, Felix, Tutz, Gerhard
core   +1 more source