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Efficient computation of high-dimensional penalized generalized linear mixed models by latent factor modeling of the random effects. [PDF]

Biometrics
Heiling HM, Rashid NU, Li Q, Peng XL, Yeh JJ, Ibrahim JG.   +5 more
europepmc   +1 more source

AquaDualPark Study - Effects of Aquatic and Land-Based Dual-Task Exercise Program on Motor and Cognitive Functions of People With Parkinson’s Disease: Protocol for a Randomized Clinical Trial

Adriano Zanardi da Silva, Vera Lúcia Israel   +1 more
openalex   +1 more source

Predicting Epileptogenic Tubers in Patients With Tuberous Sclerosis Complex Using a Fusion Model Integrating Lesion Network Mapping and Machine Learning

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Accurate localization of epileptogenic tubers (ETs) in patients with tuberous sclerosis complex (TSC) is essential but challenging, as these tubers lack distinct pathological or genetic markers to differentiate them from other cortical tubers.
Tinghong Liu, Qi Wang, Suhui Kuang, Dezhi Cao, Ping Ding, Shaohui Zhang, Haihua Wei, Zhirong Wei, Jinshan Xu, Xinyu Huang, Bing Liu, Shuli Liang   +11 more
wiley   +1 more source

Increased Blood Levels of NfL, GFAP, and Placental Growth Factor After Radiotherapy to the Brain

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT In this study, we analyzed biomarkers of neuronal, glial, and vascular injury in longitudinal paired samples of blood and cerebrospinal fluid after prophylactic cranial irradiation in patients with small cell lung cancer. Neurofilament light chain protein (NfL) and glial fibrillary acidic protein (GFAP) increased in serum and cerebrospinal ...
Erik Fernström, Thomas Björk‐Eriksson, Pontus Erickson, Jan Nyman, Henrik Zetterberg, Marie Kalm   +5 more
wiley   +1 more source

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Fixed-effect Versus Random-effects Models for Meta-analyses: Random-effects Models

European Urology Focus, 2023
Random-effects models can account for variability both within and between studies. This makes them suitable for meta-analyses in surgery, where there is often significant heterogeneity between studies or heterogeneity owing to intrinsic differences attributable to patient or population factors.
Alex L.E. Halme, Kristen McAlpine, Alberto Martini   +2 more
openaire   +2 more sources

Random Effects Models

1994
This chapter is concerned with random effects models for analyzing nonnormal data that are assumed to be clustered or correlated. The clustering may be due to repeated measurements over time, as in longitudinal studies, or to subsampling the primary sampling units, as in cross-sectional studies.
Ludwig Fahrmeir, Gerhard Tutz
openaire   +1 more source

Random Effects Models

2017
This chapter deals with the most relevant multi-dimensional random effects panel data models, where, unlike the case of fixed effects, the number of parameters to be estimated does not increase with the sample size. First, optimal (F)GLS estimators are presented for the textbook-style complete data case, paying special attention to asymptotics.
Balazsi, Laszlo, Baltagi, Badi H., Matyas, Laszlo, Pus, Daria   +3 more
openaire   +2 more sources

A note regarding ‘random effects’

Statistics in Medicine, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +3 more sources

Fixed Effects and Random Effects

2008
One of the major benefits from using panel data as compared to cross-section data on individuals is that it enables us to control for individual heterogeneity. Not controlling for these unobserved individual specific effects leads to bias in the resulting estimates.
openaire   +1 more source