Latent Variable Models for Clustered Ordinal Data
Copyright policy )Latent Variable Models for Clustered Ordinal Data
Existing methods for the analysis of clustered, ordinal data are inappropriate for certain applications. We propose latent variable models for clustered ordinal data which are derived as natural extensions of latent variable models for clustered binary data (Qu, Williams, Beck, and Medendorp, 1992. Biometrics 48, 1095-1102). These models can be applied to repeated measures data, familial data, longitudinal data, and data with both cluster specific and occasion specific covariates with a wide range of correlation structures.
- Cleveland Clinic United States
Generalized linear models (logistic models), Biometry, Time Factors, Ultraviolet Rays, Tretinoin, Applications of statistics to biology and medical sciences; meta analysis, Ointments, Double-Blind Method, Odds Ratio, Cluster Analysis, Humans, Probability, Randomized Controlled Trials as Topic, Analysis of Variance, Models, Statistical, Skin Aging, Linear inference, regression, Face, Arm, Sunlight
Generalized linear models (logistic models), Biometry, Time Factors, Ultraviolet Rays, Tretinoin, Applications of statistics to biology and medical sciences; meta analysis, Ointments, Double-Blind Method, Odds Ratio, Cluster Analysis, Humans, Probability, Randomized Controlled Trials as Topic, Analysis of Variance, Models, Statistical, Skin Aging, Linear inference, regression, Face, Arm, Sunlight
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