Bias and Precision of the "Multiple Imputation, Then Deletion" Method for Dealing With Missing Outcome Data
Thomas R Sullivan, Amy B Salter, Philip Ryan, Katherine J Lee
American Journal of Epidemiology | OXFORD UNIV PRESS INC | Published : 2015
Multiple imputation (MI) is increasingly being used to handle missing data in epidemiologic research. When data on both the exposure and the outcome are missing, an alternative to standard MI is the "multiple imputation, then deletion" (MID) method, which involves deleting imputed outcomes prior to analysis. While MID has been shown to provide efficiency gains over standard MI when analysis and imputation models are the same, the performance of MID in the presence of auxiliary variables for the incomplete outcome is not well understood. Using simulated data, we evaluated the performance of standard MI and MID in regression settings where data were missing on both the outcome and the exposure..View full abstract
Awarded by National Health and Medical Research Council
The work was supported by funding from an Australian Postgraduate Award (T.R.S.) and National Health and Medical Research Council Career Development Fellowship 1053609 (K.J.L.).