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Abstract Details
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Activity Number:
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379
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Type:
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Invited
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Date/Time:
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Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Health Policy Statistics
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| Abstract - #300150 |
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Title:
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Generating Multiple Imputations from Multiple Models to Incorporate Model Uncertainty in Nonignorable Missing Data Problems
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Author(s):
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Ofer Harel*+ and Juned Siddique
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Companies:
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University of Connecticut and Northwestern University
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Address:
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215 Glenbrook Road U-4120, Storrs, CT, 06269,
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Keywords:
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multiple imputation ;
misssing data ;
nonignorable models ;
model uncertainty
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Abstract:
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We present a framework for generating multiple imputations when the missing data are assumed to be non-ignorably missing. Imputations are generated from more than one model in order to incorporate uncertainty regarding what is the "correct" imputation model. Parameter estimates based on the diff erent imputation models are combined using the rules of nested multiple imputation. Through the use of simulation, we investigate the impact of imputation model uncertainty on post-imputation inferences and show that incorporating model uncertainty can improve the coverage of parameter estimates. We also apply our method to a longitudinal depression treatment study. Our method provides a simple approach for formalizing subjective notions regarding non-response so that they can be easily stated, communicated, and compared.
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