JSM 2004 - Toronto

Abstract #301087

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Activity Number: 77
Type: Topic Contributed
Date/Time: Monday, August 9, 2004 : 8:30 AM to 10:20 AM
Sponsor: Section on Survey Research Methods
Abstract - #301087
Title: Investigation of the Impact of Imputation on Variance Estimation in the Medical Expenditure Panel Survey
Author(s): Robert M. Baskin*+ and Lap-Ming Wun and John Sommers and Marc W. Zodet and Steven R. Machlin and Trena M. Ezzati-Rice
Companies: Agency for Healthcare Research and Quality and Agency for Healthcare Research and Quality and Agency for Healthcare Research and Quality and Agency for Healthcare Research and Quality and Agency for Healthcare Research and Quality and Agency for Healthcare Research and Quality
Address: 540 Gaither Rd., Rockville, MD, 20850,
Keywords: multiple imputation ; Rao-Shao adjustment ; hot-deck ; Bayesian bootstrap ; imputation cell ; missing at random
Abstract:

The Medical Expenditure Panel Survey (MEPS, a national probability sample survey sponsored by the Agency for Healthcare Research and Quality), is designed to provide nationally representative estimates of health care use, expenditures, sources of payment, and insurance coverage for the U.S. civilian noninstitutionalized population. MEPS, like most sample surveys, experiences unit and item nonresponse despite efforts to maximize response. At the present, a form of a weighted sequential hot-deck is used to impute missing values of health care expenditures, along with other variables, but currently it is difficult to assess the impact that imputation has on the variance estimates produced from MEPS data. The purpose of this study is to begin an assessment of the impact of this imputation on estimates of variance for estimates of health care expenditures as well as other key variables that are imputed. The assessment involves an evaluation of estimated variances using the Rao-Shao adjustment for imputation within replicates and variances produced by multiple imputation versus the "naïve" estimate of the variances that treat the imputed values as if they were observed.

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