A1443
Title: Finite Markov chains with absorbing states and misspecified random effects with an application to cognitive data
Authors: Pei Wang - Bowling Green State University (United States) [presenting]
Abstract: Finite Markov chains with absorbing states are valuable tools for analyzing longitudinal data with categorical responses. However, defining the one-step transition probabilities in terms of fixed and random effects presents challenges due to the large number of unknown parameters involved. To address this, a marginal model is employed to estimate the fixed effects across various choices of the distribution governing the random effects. Subsequently, an h-likelihood method is utilized to estimate the random effects based on these fixed effect estimates. The estimation approach is applied to analyze longitudinal cognitive data from the Nun Study. The findings highlight that the fixed effects remain relatively robust across a wide range of assumptions. However, the analysis of random effects utilizing tools such as AIC, Q-Q plots, and gradient plots appears to be sensitive to misspecifications in the distribution of the random effects. The proposed approach allows researchers to verify the assumptions of random effects and provides more accurate estimation of these effects.
