HCatalyst: Alan Agresti

Modeling Ordinal Categorical Data (Day 1) Harvard Catalyst Biostatistics Seminar This short course surveys methods for modeling categorical response variables that have a natural ordering of the categories. Such data often occur in the social sciences (e.g., for measuring attitudes and opinions) and in medical and public health disciplines (e.g., pain, quality of life, severity of a condition). Topics to be covered include logistic regression models using cumulative logits with proportional odds structure, non-proportional odds models, other ordinal logistic regression models such as using adjacent-categories logits, other multinomial response models such as the cumulative probit, and marginal models and random effects models for clustered, correlated ordinal responses (e.g., repeated measurement data). Examples presented include social survey data and randomized clinical trials. Software focus is on R, but SAS output is also provided for many examples. The course will be a concise summary of parts of the book, "Analysis of Ordinal Categorical Data" by Alan Agresti (2nd ed., Wiley, 2010). RSVP required.