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Simultaneous Inference in General Parametric Models [PDF]
, 2008 Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre-specified significance level ...
Bates +29 more
core +3 more sources
Meta-analysis of proportions using generalized linear mixed models
Epidemiology, 2020 Supplemental Digital Content is available in the text. Epidemiologic research often involves meta-analyses of proportions. Conventional two-step methods first transform each study’s proportion and subsequently perform a meta-analysis on the transformed ...
Lifeng Lin, H. Chu
semanticscholar +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 +2 more
doaj +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Purpose Pediatric central nervous system (CNS) tumors often recur despite multimodality therapy. Although re‐irradiation (re‐RT) has historically been limited by concerns for severe late toxicities, modern techniques have renewed interest in this approach. Proton therapy provides dosimetric advantages that may enable curative re‐treatment with
Jin‐Ho Song +15 more
wiley +1 more source
Identifying genetically driven clinical phenotypes using linear mixed models
Nature Communications, 2016 Use of general linear mixed models (GLMMs) in genetic variance analysis can quantify the relative contribution of additive effects from genetic variation on a given trait.
Jonathan D. Mosley +13 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
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
Pediatric Blood &Cancer, EarlyView.ABSTRACT Treatment‐associated hepatotoxicity (TAH) is a common complication of pediatric acute lymphoblastic leukemia (ALL) treatment, but genetic risk factors remain poorly understood. We evaluated the SOD2 rs4880 variant in 544 children with ALL at Texas Children's Hospital. After adjusting for demographic and clinical covariates, the rs4880 C allele
Emily J. Mason +14 more
wiley +1 more source
Modeling Multiple Item Context Effects With Generalized Linear Mixed Models
Frontiers in Psychology, 2019 Item context effects refer to the impact of features of a test on an examinee's item responses. These effects cannot be explained by the abilities measured by the test. Investigations typically focus on only a single type of item context effects, such as
Norman Rose +6 more
doaj +1 more source
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 +2 more
doaj +1 more source