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Analysis of longitudinal data
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TLDR
In this paper, a generalized linear model for longitudinal data and transition models for categorical data are presented. But the model is not suitable for categric data and time dependent covariates are not considered.Abstract:
1. Introduction 2. Design considerations 3. Exploring longitudinal data 4. General linear models 5. Parametric models for covariance structure 6. Analysis of variance methods 7. Generalized linear models for longitudinal data 8. Marginal models 9. Random effects models 10. Transition models 11. Likelihood-based methods for categorical data 12. Time-dependent covariates 13. Missing values in longitudinal data 14. Additional topics Appendix Bibliography Indexread more
Citations
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Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin.
William C. Knowler,Elizabeth Barrett-Connor,Sarah E. Fowler,Richard F. Hamman,John M. Lachin,Elizabeth A. Walker,David M. Nathan +6 more
TL;DR: In this paper, the authors compared a lifestyle intervention with metformin to prevent or delay the development of Type 2 diabetes in nondiabetic individuals. And they found that the lifestyle intervention was significantly more effective than the medication.
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Design and validation of a histological scoring system for nonalcoholic fatty liver disease
David E. Kleiner,Elizabeth M. Brunt,Mark L. Van Natta,Cynthia Behling,Melissa J. Contos,Oscar W. Cummings,Linda D. Ferrell,Yao Chang Liu,Michael Torbenson,Aynur Unalp-Arida,Matthew M. Yeh,Arthur J. McCullough,Arun J. Sanyal +12 more
TL;DR: A strong scoring system and NAS for NAFLD and NASH with reasonable inter‐rater reproducibility that should be useful for studies of both adults and children with any degree ofNAFLD are presented.
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Applied Longitudinal Analysis
TL;DR: In this article, the authors present an overview of linear models for long-term continuous-time data and compare them with generalized linear mixed effects models for estimating the covariance and the mean.
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Using SAS PROC MIXED to Fit Multilevel Models, Hierarchical Models, and Individual Growth Models
TL;DR: This paper is written as a step-by-step tutorial that shows how to fit the two most common multilevel models: (a) school effects models, designed for data on individuals nested within naturally occurring hierarchies (e.g., students within classes); and (b) individual growth models,designed for exploring longitudinal data (on individuals) over time.
References
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Book
Analysis of Repeated Measures
Martin Crowder,David J. Hand +1 more
TL;DR: In this paper, the authors present a simple analysis of individual times response feature analysis and individual curve fitting for polynomial trends Manova, and two-stage linear models: random regression coefficients estimation and testing particular aspects examples.
Book
Practical Longitudinal Data Analysis
David J. Hand,Martin Crowder +1 more
TL;DR: In this article, the authors present a method for estimating normal error distributions of continuous non-normal measures. But their method is based on a generalized linear model and Maximum Quasi-Likelihood Estimation.