JSM 2005 - Toronto

Abstract #302756

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Activity Number: 45
Type: Invited
Date/Time: Sunday, August 7, 2005 : 4:00 PM to 5:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #302756
Title: Selection-bias-corrected Models for Evaluating Weighting Class and Multiple Imputation Methods of Adjusting for Partial Response when Missing Data are Not Missing at Random
Author(s): Philip J. Smith*+ and Lawrence Marsh
Companies: Centers for Disease Control and Prevention and University of Notre Dame
Address: National Immunization Program, Atlanta, GA, 30333,
Keywords: Selection bias
Abstract:

Health surveys often collect data in two stages. In the first, demographic information is collected and permission is requested to obtain information on health outcomes, Y, from respondents' health care providers. When consent is obtained, providers are contacted and Y is collected. A "complete response" results when the first and second phases are completed. A "partial response" results when only the first phase is completed. Potential differences between complete and partial responders typically are taken into account by using weighting-class methods, which assume the missing values are missing at random (MAR). Alternatively, the missing values may be imputed using a predicted mean. Imputation models often make the implicit assumption that missing values are MAR. This paper describes an imputation model that accounts for the possibility that the missing values are not MAR. A statistical test for evaluating the MAR assumption is given.

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