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Empirical Likelihood for Generalized Linear Models with Longitudinal Data
Generalized linear models are usually adopted to model the discrete or nonnegative responses. In this paper, empirical likelihood inference for fixed...
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coda4microbiome: compositional data analysis for microbiome cross-sectional and longitudinal studies
BackgroundOne of the main challenges of microbiome analysis is its compositional nature that if ignored can lead to spurious results. Addressing the...
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Hidden Markov models for longitudinal rating data with dynamic response styles
This work deals with the analysis of longitudinal ordinal responses. The novelty of the proposed approach is in modeling simultaneously the temporal...
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Longitudinal individual predictions from irregular repeated measurements data
Intensive longitudinal data can be used to explore important associations and patterns between various types of inputs and outcomes. Nonlinear...
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Clustering Longitudinal Data for Growth Curve Modelling by Gibbs Sampler and Information Criterion
Clustering longitudinal data for growth curve modelling is considered in this paper, where we aim to optimally estimate the underpinning unknown...
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Joint Representation Learning with Generative Adversarial Imputation Network for Improved Classification of Longitudinal Data
Generative adversarial networks (GANs) have demonstrated their effectiveness in generating temporal data to fill in missing values, enhancing the...
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Analysis of the HIV/AIDS Data Using Joint Modeling of Longitudinal (k,l)-Inflated Count and Time to Event Data in Clinical Trials
Generalized linear mixed effect models (GLMEMs) are widely applied for the analysis of correlated non-Gaussian data such as those found in...
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The Development of Longitudinal Youth Research
This chapter traces the evolution of longitudinal youth research design and practice, drawing on insights from Australia and the UK since the 1940s....
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Accurate prediction of HCC risk after SVR in patients with hepatitis C cirrhosis based on longitudinal data
BackgroundMost existing predictive models of hepatocellular carcinoma (HCC) risk after sustained virologic response (SVR) are built on data collected...
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Two-Part Mixed Effects Mixture Model for Zero-Inflated Longitudinal Compositional Data
Compositional data (CD) is mostly analyzed using ratios of components and log-ratio transformations to apply known multivariable statistical methods....
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Clustering longitudinal ordinal data via finite mixture of matrix-variate distributions
In social sciences, studies are often based on questionnaires asking participants to express ordered responses several times over a study period. We...
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Insights From Longitudinal Research
This chapter concludes the section Insights from youth longitudinal research, highlighting some key points made by the three chapters presented...
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The Causal Relationship Between Volunteering and Social Cohesion: A Large Scale Analysis of Secondary Longitudinal Data
It is often taken for granted that social cohesion and volunteering are inextricably related. Previous research suggests both that social cohesion...
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Robust semiparametric modeling of mean and covariance in longitudinal data
Longitudinal data often suffer from heavy-tailed errors and outliers, which can significantly reduce efficiency and lead to invalid inferences....
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Nonparametric longitudinal regression model to analyze shape data using the Procrustes rotation
Shape, as an intrinsic concept, can be considered as a source of information in some statistical analysis contexts. For instance, one of the...
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Multivariate Longitudinal Microbiome Models
Chapter 15 mainly introduced the glmmTMB, GLMMadaptive, and FZINBMM packages. However, all these three packages...
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A bias-reduced generalized estimating equation approach for proportional odds models with small-sample longitudinal ordinal data
BackgroundLongitudinal ordinal data are commonly analyzed using a marginal proportional odds model for relating ordinal outcomes to covariates in the...
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Using collective intelligence methods to improve government data infrastructures and promote the use of complex data: The example of the Northern Ireland Longitudinal Study
BackgroundThis paper discusses how collective intelligence (CI) methods can be implemented to improve government data infrastructures, not only to...
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Psychosocial Health Outcomes of Children Following Family Reunification: Longitudinal Analysis of Randomised Controlled Trial Data
This longitudinal study examined the psychosocial health trajectories of children following reunification from residential care and the associated...
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Detecting influential subjects in intensive longitudinal data using mixed-effects location scale models
BackgroundCollection of intensive longitudinal health outcomes allows joint modeling of their mean (location) and variability (scale). Focusing on...
