JSM 2005 Online Program

Abstract #302984

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Activity Number:	190
Type:	Contributed
Date/Time:	Monday, August 8, 2005 : 2:00 PM to 3:50 PM
Sponsor:	General Methodology
Abstract - #302984
Title:	Robust Model-based Analysis of General Pattern Missing Data
Author(s):	Hyonggin An*+ and Roderick J. Little
Companies:	The University of Iowa and University of Michigan
Address:	200 Hawkins Drive, Department of Biostatistics, Iowa City, IA, 52252, United States
Keywords:	missing data ; penalized spline ; multiple imputation ; sequential regression multivariate imputation ; Gibbs' sampler
Abstract:	Little and An (2004) proposed a method that uses penalized splines of propensity score to yield robust model-based inference under the missing at random (MAR), assuming monotone missing data. In this paper, we consider extending the method to general pattern missing data using multiple imputation via Markov chain Monte Carlo methods. Unlike monotone pattern, the full conditional distributions for variables with general pattern missing values, propensity scores, and parameters are difficult to obtain. To address this difficulty, we adapt and modify the sequential regression multivariate imputation (SRMI) approach of Raghunathan et al. (2001 to create multiply imputed datasets. Simulation comparisons with other methods will be provided.

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