Journal ArticleDOI
A random effects model for ordinal responses from a crossover trial
Farkad Ezzet,John Whitehead +1 more
TLDR
In this article, the authors investigate a random effects model and show that the model is simple and general, and interpretation of parameters is easy, though with a complicated fitting procedure.Abstract:
Crossover studies have been successfully conducted in the case of continuous responses. Existing procedures of analysis for ordinal responses, on the other hand, are rarely satisfactory unless strict, usually unrealistic, assumptions are made. In this paper we investigate a random effects model and show that the model is simple and general. Interpretation of parameters is easy, though with a complicated fitting procedure.read more
Citations
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Journal ArticleDOI
brms: An R Package for Bayesian Multilevel Models Using Stan
TL;DR: The brms package implements Bayesian multilevel models in R using the probabilistic programming language Stan, allowing users to fit linear, robust linear, binomial, Poisson, survival, ordinal, zero-inflated, hurdle, and even non-linear models all in a multileVEL context.
Journal ArticleDOI
Application of Random-Effects Pattern-Mixture Models for Missing Data in Longitudinal Studies
Donald Hedeker,Robert D. Gibbons +1 more
TL;DR: Use of random-effects pattern-mixture models to further handle and describe the influence of missing data in longitudinal studies is described.
Journal ArticleDOI
A random-effects ordinal regression model for multilevel analysis.
Donald Hedeker,Robert D. Gibbons +1 more
TL;DR: A random-effects ordinal regression model is proposed for analysis of clustered or longitudinal ordinal response data and a maximum marginal likelihood (MML) solution is described using Gauss-Hermite quadrature to numerically integrate over the distribution of random effects.
Journal ArticleDOI
MIXOR: a computer program for mixed-effects ordinal regression analysis
Donald Hedeker,Robert D. Gibbons +1 more
TL;DR: MIXOR provides maximum marginal likelihood estimates for mixed-effects ordinal probit, logistic, and complementary log-log regression models, used for analysis of dichotomous and ordinal outcomes from either a clustered or longitudinal design.
Journal ArticleDOI
A mixed-effects multinomial logistic regression model.
TL;DR: A mixed-effects multinomial logistic regression model is described for analysis of clustered or longitudinal nominal or ordinal response data and is parameterized to allow flexibility in the choice of contrasts used to represent comparisons across the response categories.
References
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Journal ArticleDOI
The Analysis of Variance
TL;DR: In this paper, the basic theory of analysis of variance by considering several different mathematical models is examined, including fixed-effects models with independent observations of equal variance and other models with different observations of variance.
Journal ArticleDOI
Regression Models for Ordinal Data
TL;DR: In this article, a general class of regression models for ordinal data is developed and discussed, which utilize the ordinal nature of the data by describing various modes of stochastic ordering and this eliminates the need for assigning scores or otherwise assuming cardinality instead of ordinality.
Journal Article
Multivariate generalizations of the proportional hazards model
David Clayton,Jack Cuzick +1 more
TL;DR: In this paper, a new approach to the analysis of bivariate survival data is presented, which involves the development of a model for bivariate life-tables with a single association parameter which is unaffected by monotone transformation of the marginal distributions.
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A random-effects ordinal regression model for multilevel analysis.
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