Bayesian Inference for Stereotype Regression

• Activity Number: 222
• Type: Topic Contributed
• Date/Time: Monday, August 2, 2010 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Bhramar Mukherjee*+
• Companies: University of Michigan
• Keywords: Conditional likelihood ; Non-ignorable missingness ; Proportional odds ; Stages of cancer ; Vector generalized linear model.

Abstract

With advances in modern medicine and clinical diagnosis, case-control data with characterization of finer subtypes of cases are often available. In matched case-control studies, missingness in exposure values often leads to deletion of entire stratum,and thus entails a significant loss in information. When subtypes of cases are treated as categorical outcomes,the data are further stratified and deletion of observations becomes even more expensive in terms of precision of the category-specific odds-ratio parameters,especially using the multinomial logit model. The stereotype regression model for categorical responses lies intermediate between the proportional odds and the multinomial or baseline category logit model.The use of this class of models has been limited as the structure of the model implies certain inferential challenges.