When data are not missing at random: implications for measuring health conditions in the Behavioral Risk Factor Surveillance System.

ABSTRACT:

Objectives

To examine the effect on estimated levels of health conditions produced from large-scale surveys, when either list-wise respondent deletion or standard demographic item-level imputation is employed. To assess the degree to which further bias reduction results from the inclusion of correlated ancillary variables in the item imputation process.

Design

Large cross-sectional (US level) household survey.

Participants

218 726 US adults (18 years and older) in the 2006 Behavioral Risk Factor Surveillance System Survey. This survey is the largest US telephone survey conducted by the Centers for Disease Control and Prevention.

Primary and secondary outcome measures

Estimated rates of severe depression among US adults.

Results

The use of list-wise respo

SUBMITTER: Frankel MR 

PROVIDER: S-EPMC3400062 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature