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Bayesian inference for the stereotype regression model: Application to a case–control study of prostate cancer by Jaeil Ahn; Bhramar Mukherjee; Mousumi Banerjee; Kathleen A. Cooney is a Mathematics article available to read on EtoBox.

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## Abstract The stereotype regression model for categorical outcomes, proposed by Anderson (__J. Roy. Statist. Soc. B.__ 1984; **46**:1–30) is nested between the baseline‐category logits and adjacent category logits model with proportional odds structure. The stereotype model is more parsimonious than the ordinary baseline‐category (or multinomial logistic) model due to a product representation of the log‐odds‐ratios in terms of a common parameter corresponding to each predictor and category‐specific scores. The model could be used for both ordered and unordered outcomes. For ordered outcomes, the stereotype model allows more flexibility than the popular proportional odds model in capturing highly subjective ordinal scaling which does not result from categorization of a single latent variable, but are inherently multi‐dimensional in nature. As pointed out by Greenland (__Statist. Med.__ 1994; **13**:1665–1677), an additional advantage of the stereotype model is that it provides unbiased and valid inference under outcome‐stratified sampling as in case–control studies. In addition, for matched case–control studies, the stereotype model is amenable to classical conditional likelihood

Who reads Bayesian inference for the stereotype regression model: Application to a case–control study of prostate cancer?

Author

Jaeil Ahn; Bhramar Mukherjee; Mousumi Banerjee; Kathleen A. Cooney

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; Wiley (ISSN 0277-6715)

Published

2009

Language

EN

Field

Mathematics (Physical Sciences)