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

Abstract Details

Activity Number: 29
Type: Contributed
Date/Time: Sunday, August 1, 2010 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #309208
Title: Longitudinal Factor Analysis of Continuous and Ordinal Data
Author(s): Bradley J. Barney*+ and Valen Johnson and Simon J. Sheather and Veera Baladandayuthapani and Xin Shelley Wang and Charles S. Cleeland
Companies: Texas A&M University and MD Anderson Cancer Center and Texas A&M University and MD Anderson Cancer Center and MD Anderson Cancer Center and MD Anderson Cancer Center
Address: 1805 Potomac Pl. Apt. B, College Station, TX, 77840,
Keywords: factor analysis ; Bayesian ; longitudinal data ; ordinal data

The M.D. Anderson Symptom Inventory (MDASI) is an instrument used to assess a patient's symptom burden for a host of symptoms. Data were collected over time on twenty-five symptoms (from the MDASI) and on seven cytokines (from lab measurements). Interest lies in describing comovements of the symptom levels and cytokine measurements over time. We use a factor analysis model that allows factor scores to be autocorrelated over time, thereby allowing for dependence across symptoms/cytokines and across time. We treat symptom levels as ordinal data and cytokine measurements as continuous data. We use the Bayesian paradigm for parameter estimation and for modeling a small number of missing symptom levels. Along with our particular application, the model framework is of interest because of its potential to explain many different data dependence structures with relatively few parameters.

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