Abstract Number:
2633
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Svetlana Masalovich (1), Alissa O'Halloran (1), Shikha Garg (1), Catherine Bozio (1)
Institutions:
(1) Centers for Disease Control and Prevention, Atlanta
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
Multiple imputation (MI) of a variable with multiple categories can be accomplished in several ways. In the Influenza Hospitalization Surveillance Network, influenza type/subtype and race/ethnicity are two multicategory variables subject to missingness. MI of each of these variables presents challenges: influenza type/subtype variable is derived from two categorical variables, only one of which has missing data; MI of race/ethnicity in the absence of good auxiliary variables may result in biased estimates. Additionally, surveillance data are collected by stratified sampling. Imputing influenza type/subtype using a principled method, while accounting for sampling design and achieving compatibility between the MI and the analysis models can be challenging. We explored strategies for imputing missing data for these two variables. We used a simulation study to compare the performance of the selected approaches. Although the proportion of observations with missing subtype was high, the missing mechanism was likely missing at random (MAR); thus, we evaluated the benefits of using MI compared to a complete case analysis. Additionally, we explored methods for MI of missing race/ethnicity.
Keywords:
Multiple imputation|multicategory variables | | | |
Sponsors:
Section on Statistics in Epidemiology
Tracks:
Miscellaneous
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