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.
Qu
Y
Y
Department of Biostatistics and Epidemiology, Cleveland Clinic Foundation, Ohio 44195, USA.
Piedmonte
M R
MR
Medendorp
S V
SV
D000704:Analysis of Variance; D001132:Arm; D001699:Biometry / Q000379:methods; D016000:Cluster Analysis; D004311:Double-Blind Method; D005145:Face; D006801:Humans; D015233:Models, Statistical; D016017:Odds Ratio; D009824:Ointments; D011336:Probability; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D015595:Skin Aging / Q000187:drug effects / Q000528:radiation effects; D013472:Sunlight / Q000009:adverse effects; D013997:Time Factors; D014212:Tretinoin / Q000008:administration & dosage / Q000627:therapeutic use; D014466:Ultraviolet Rays
D009824:Ointments; D014212:Tretinoin