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Statistical analysis of longitudinal neuroimage data with linear mixed effects models
- NeuroImage
"... Longitudinal neuroimaging (LNI) studies are rapidly becoming more prevalent and growing in size. Today, no standardized computational tools exist for the analysis of LNI data and widely used methods are sub-optimal for the types of data encountered in real-life studies. Linear Mixed Effects (LME) m ..."
Abstract
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Cited by 6 (2 self)
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Longitudinal neuroimaging (LNI) studies are rapidly becoming more prevalent and growing in size. Today, no standardized computational tools exist for the analysis of LNI data and widely used methods are sub-optimal for the types of data encountered in real-life studies. Linear Mixed Effects (LME
Nonlinear Models for Repeated Measurement Data
, 1995
"... Nonlinear mixed effects models for data in the form of continuous, repeated measurements on each of a number of individuals, also known as hierarchical nonlinear models, are a popular platform for analysis when interest focuses on individual-specific characteristics. This framework first enjoyed wid ..."
Abstract
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Cited by 338 (9 self)
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Nonlinear mixed effects models for data in the form of continuous, repeated measurements on each of a number of individuals, also known as hierarchical nonlinear models, are a popular platform for analysis when interest focuses on individual-specific characteristics. This framework first enjoyed
Categorical Data Analysis: Away from ANOVAs (transformation or not) and towards Logit Mixed Models
"... This paper identifies several serious problems with the widespread use of ANOVAs for the analysis of categorical outcome variables such as forced-choice variables, question-answer accuracy, choice in production (e.g. in syntactic priming research), et cetera. I show that even after applying the arc ..."
Abstract
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Cited by 252 (7 self)
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categorical data and offer many advantages over ANOVA. Unfortunately, ordinary logit models do not include random effect modeling. To address this issue, I describe mixed logit models (Generalized Linear Mixed Models for binomially distributed outcomes, Breslow & Clayton, 1993), which combine
G: Models for Discrete Longitudinal Data
- and Chen 6 © 2012 by American Society of Clinical Oncology JOURNAL OF CLINICAL ONCOLOGY
"... This book covers a wide variety of statistical techniques for longitudinal data analysis. The authors, Geert Molenberghs and Geert Verbeke –both well known in this field – have extended their previous textbook (Verbeke and Molenberghs, 1997), mainly focused on linear mixed model for continuous data, ..."
Abstract
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Cited by 172 (16 self)
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This book covers a wide variety of statistical techniques for longitudinal data analysis. The authors, Geert Molenberghs and Geert Verbeke –both well known in this field – have extended their previous textbook (Verbeke and Molenberghs, 1997), mainly focused on linear mixed model for continuous data
Generalized linear mixed models: a practical guide for ecology and evolution.
- Trends in Ecology and Evolution,
, 2009
"... How should ecologists and evolutionary biologists analyze nonnormal data that involve random effects? Nonnormal data such as counts or proportions often defy classical statistical procedures. Generalized linear mixed models (GLMMs) provide a more flexible approach for analyzing nonnormal data when ..."
Abstract
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Cited by 183 (1 self)
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How should ecologists and evolutionary biologists analyze nonnormal data that involve random effects? Nonnormal data such as counts or proportions often defy classical statistical procedures. Generalized linear mixed models (GLMMs) provide a more flexible approach for analyzing nonnormal data when
General Multi-Level Linear Modelling for Group Analysis in FMRI
- NeuroImage
, 2003
"... This paper discusses general modelling of multi-subject and/or multi-session FMRI data. In particular, we show that a two-level mixed-effects model (where parameters of interest at the group level are estimated from parameter and variance estimates from the single-session level) can be made equivale ..."
Abstract
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Cited by 209 (8 self)
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This paper discusses general modelling of multi-subject and/or multi-session FMRI data. In particular, we show that a two-level mixed-effects model (where parameters of interest at the group level are estimated from parameter and variance estimates from the single-session level) can be made
Temporal autocorrelation in univariate linear modelling of fMRI data
- pP Y C W P k nk N p Var(Yk ) (Yk ) 0 1 C CR 1 Var(Y ) P k nk N Var(Y k ) 0 1 C MI H(X;Y ) H(X) H(Y ) 1 0 C NMI H(X;Y ) H(X)+H(Y
, 2000
"... In functional magnetic resonance imaging statistical analysis there are problems with accounting for temporal autocorrelations when assessing change within voxels. Techniques to date have utilized temporal filtering strategies to either shape these autocorrelations or remove them. Shaping, or “color ..."
Abstract
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Cited by 211 (10 self)
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In functional magnetic resonance imaging statistical analysis there are problems with accounting for temporal autocorrelations when assessing change within voxels. Techniques to date have utilized temporal filtering strategies to either shape these autocorrelations or remove them. Shaping
Application of hierarchical linear models to assessing change.
- Psychological Bulletin,
, 1987
"... Recent advances in the statistical theory of hierarchical linear models should enable important breakthroughs in the measurement of psychological change and the study of correlates of change. A two-stage model of change is proposed here. At the first, or within-subject stage, an individual's s ..."
Abstract
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Cited by 207 (5 self)
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Recent advances in the statistical theory of hierarchical linear models should enable important breakthroughs in the measurement of psychological change and the study of correlates of change. A two-stage model of change is proposed here. At the first, or within-subject stage, an individual
LSTGEE: Longitudinal Analysis of Neuroimaging Data
"... Longitudinal imaging studies are essential to understanding the neural development of neuropsychiatric disorders, substance use disorders, and normal brain. Using appropriate image processing and statistical tools to analyze the imaging, behavioral, and clinical data is critical for optimally explor ..."
Abstract
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, for the analysis of neuroimaging data from longitudinal studies. We develop generalized estimating equations for jointly modeling imaging measures with behavioral and clinical variables from longitudinal studies. We develop a test procedure based on a score test statistic and a resampling method to test linear
Linear Mixed Models for Longitudinal Data with Nonrandom Dropouts
, 2006
"... Abstract: Longitudinal studies represent one of the principal research strategies employed in medical and social research. These studies are the most appropriate for studying individual change over time. The prematurely withdrawal of some subjects from the study (dropout) is termed nonrandom when t ..."
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the probability of missingness depends on the missing value. Nonrandom dropout is common phenomenon associated with longitudinal data and it complicates statistical inference. Linear mixed effects model is used to fit longitudinal data in the presence of nonrandom dropout. The stochastic EM algorithm is developed
Results 1 - 10
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2,299
