Dynamic Latent Trait Models with Mixed Hidden Markov Structure for Mixed Longitudinal Outcomes

• Activity Number: 230
• Type: Topic Contributed
• Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Yue Zhang*+ and Kiros Berhane
• Companies: University of Southern California and University of Southern California
• Keywords: Mixed Hidden Markov Model (MHMM) ; Mixed Outcomes ; Differential Misclassification ; MCMC ; Lung Function Measurements ; Asthma

Abstract

We present a general joint modeling approach to model mixed longitudinal outcomes from the exponential family in ways that take into account any differential misclassification that may exist among some categorical outcomes. Under this general modeling framework, outcomes observed without measurement error are related to latent trait variables through generalized linear mixed effect models. The misclassified outcomes are related to the latent class variables, which represent unobserved real states, using mixed hidden Markov models (MHMM). A transition modeling structure allows the latent traits and latent class variables to depend on observed predictors at the same time period and also on latent traits and latent class variables at previous time periods for each individual. Inference for this proposed approach is based on Bayesian principles. This approach is applied to data from the Southern California Children Health Study (CHS) in order to gain better insight about the underlying biological mechanism that governs the inter-relationship between asthma state and lung function development and how both might be affected by ambient air pollutant exposure.