Journal ArticleDOI
Analysis of longitudinal data. Beyond MANOVA.
TLDR
Routine use of MANOVA for the analysis of longitudinal data, particularly when there is a substantial proportion of drop-outs, is ill advised and psychiatric researchers dealing with such data should be aware of the advantages of the newer methods.Abstract:
BACKGROUND Longitudinal data arise frequently in psychiatric investigations, and are most often analysed by multivariate analysis of variance (MANOVA) procedures However, as routinely applied, the method is not satisfactory, particularly when the data are affected by subjects dropping-out of the study More suitable methods are now available
METHOD Problems with the MANOVA approach are discussed and the advantages of alternative procedures stressed
RESULTS Using MANOVA on complete cases to analyse unbalanced longitudinal data can be seriously misleading More recently developed methods are far more suitable, but only if the missing values are non-informative
CONCLUSIONS Routine use of MANOVA for the analysis of longitudinal data, particularly when there is a substantial proportion of drop-outs, is ill advised Statisticians have considerably enriched the available methodologies during the past decade, and psychiatric researchers dealing with such data should be aware of the advantages of the newer methodsread more
Citations
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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.
Journal ArticleDOI
Methods to account for spatial autocorrelation in the analysis of species distributional data : a review
Carsten F. Dormann,Jana M. McPherson,Miguel B. Araújo,Roger Bivand,Janine Bolliger,Gudrun Carl,Richard G. Davies,Alexandre H. Hirzel,Walter Jetz,W. Daniel Kissling,Ingolf Kühn,Ralf Ohlemüller,Pedro R. Peres-Neto,Björn Reineking,Boris Schröder,Frank M. Schurr,Robert J. Wilson +16 more
TL;DR: In this paper, the authors describe six different statistical approaches to infer correlates of species distributions, for both presence/absence (binary response) and species abundance data (poisson or normally distributed response), while accounting for spatial autocorrelation in model residuals: autocovariate regression; spatial eigenvector mapping; generalised least squares; (conditional and simultaneous) autoregressive models and generalised estimating equations.
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Model-based Geostatistics
TL;DR: An overview of model-based geostatistics can be found in this paper, where a generalized linear model is proposed for estimating geometrical properties of geometrically constrained data.
Journal ArticleDOI
Generalized additive models for location, scale and shape
TL;DR: The generalized additive model for location, scale and shape (GAMLSS) as mentioned in this paper is a general class of statistical models for a univariate response variable, which assumes independent observations of the response variable y given the parameters, the explanatory variables and the values of the random effects.
Journal ArticleDOI
Effect of blood pressure lowering and antihypertensive drug class on progression of hypertensive kidney disease: Results from the AASK trial
Jackson T. Wright,George L. Bakris,Tom Greene,L. Y. Agodoa,Lawrence J. Appel,Jeanne Charleston,De Anna Cheek,Janice G. Douglas-Baltimore,J. Gassman,Richard J. Glassock,Lee Hebert,Kenneth Jamerson,Julia B. Lewis,Robert A. Phillips,Robert D. Toto,John P. Middleton,Stephen G. Rostand +16 more
TL;DR: Angiotensin-converting enzyme inhibitors appear to be more effective than beta-blockers or dihydropyridine calcium channel blockers in slowing GFR decline in hypertension.
References
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Statistical Analysis with Missing Data
TL;DR: This work states that maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse and large-Sample Inference Based on Maximum Likelihood Estimates is likely to be high.
Journal ArticleDOI
Longitudinal data analysis using generalized linear models
Kung Yee Liang,Scott L. Zeger +1 more
TL;DR: In this article, an extension of generalized linear models to the analysis of longitudinal data is proposed, which gives consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence.
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Analysis of longitudinal data
TL;DR: 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.
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Applied Multivariate Data Analysis
TL;DR: In this article, applied multivariate data analysis was used to analyze the performance of a multivariate dataset in the context of data mining and analysis in the field of applied multi-dimensional data analysis.
Journal ArticleDOI
Unbalanced repeated-measures models with structured covariance matrices
TL;DR: This work addresses the question of how to analyze unbalanced or incomplete repeated-measures data through maximum likelihood analysis using a general linear model for expected responses and arbitrary structural models for the within-subject covariances.