This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 360
Type: Contributed
Date/Time: Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #309434
Title: Analyzing Cognitive Testing Data with Extensions of Item Response Theory Models
Author(s): Jonathan Gruhl*+ and Elena A. Erosheva and Paul K. Crane
Companies: University of Washington and University of Washington and University of Washington
Address: 5608 15th Ave NE, Seattle, WA, 98105, United States
Keywords: Item Response Theory ; Latent Variable Models ; Mixed Outcomes ; Bayesian Estimation ; Cognitive Functioning ; Psychometrics

Cognitive tests are important and commonly used instruments in studies of brain disorders. Analogously to data from educational tests, one could use extensions of Item Response Theory (IRT) models to analyze cognitive testing outcomes. We demonstrate this on the analysis of data from the Subcortical Ischemic Vascular Disease (SIVD) study. Drawing on recent developments in IRT modeling, we extend IRT models to allow for not only a mixture of binary and ordinal outcomes but also count, continuous, and right censored outcomes. We use a generalized linear latent variable approach for model formulation and estimate model parameters in a Bayesian framework via Metropolis-Hastings and Gibbs sampling. We compare our results to those from a simpler IRT approach that treats all outcomes as ordinal as well as to results from a more general approach that relies on extended rank likelihood methods.

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