JSM 2005 - Toronto

Abstract #303373

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 227
Type: Contributed
Date/Time: Tuesday, August 9, 2005 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #303373
Title: A Bayesian Model for Clustered Longitudinal Ordinal Data Subject to Nonignorable Missing Data Mechanism
Author(s): Niko Kaciroti*+ and Trivellore Raghunathan
Companies: University of Michigan and University of Michigan
Address: , Ann Arobor, MI, 48104,
Keywords: Pattern-mixture models ; Transition Markov models ; Gibbs Sampling ; Randomized trial ; Quality of life
Abstract:

A randomized longitudinal study was conducted to evaluate the effects of an intervention program on asthma-related outcomes. The study focuses on several outcomes where, typically, missing data remained a pervasive problem. This paper presents a method for analyzing clustered longitudinal data with missing values that result from a nonignorable missing data mechanism. The transition Markov model with random effects is used to investigate changes in ordinal outcomes over time. A Bayesian pattern-mixture model, with the flexibility to incorporate models for missing data, is used in the analysis. The nonignorable missing data mechanism is posited with easy-to-understand parameters, the cumulative odds ratio parameter between the r-th missing data pattern, and the complete data pattern. Different prior distributions on cumulative odds ratio are used to perform sensitivity analysis. The data from the Asthma Intervention Study is analyzed to explore the effect of the intervention program on improving Quality of Life (QOL).

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Revised March 2005