Biostatistics Weekly Seminar

Ordinal cumulative probability models (CPMs) such as the proportional odds regression model are typically used for discrete ordered outcomes, but can accommodate both continuous and mixed discrete/continuous outcomes. Recent papers by Liu et al. and Tian et al. describe the advantages of ordinal CPMs in this setting using non-parametric maximum likelihood estimation (NPMLE), however the extension to Bayesian inference has not been thoroughly explored. We formulate a Bayesian CPM for continuous or mixed outcome data. Bayesian CPMs have advantages over frequentist CPMs with regard to interpretation, flexibility/extendability, and exact inference (within simulation error) when the log-likelihood is not well-approximated by a quadratic function. We explore characteristics of the Bayesian CPM through simulations and a case study using HIV biomarker data.

MRBIII, Room 1220
13 November 2019
1:30pm