Journal of Biometrics & Biostatistics

Neal Axon

Publications

• Research Article
  Lessons Learned in Dealing with Missing Race Data: An Empirical Investigation
  Author(s): Mulugeta Gebregziabher, Yumin Zhao, Neal Axon, Gregory E. Gilbert, Carrae Echols and Leonard E. EgedeMulugeta Gebregziabher, Yumin Zhao, Neal Axon, Gregory E. Gilbert, Carrae Echols and Leonard E. Egede
  
  Abstract Background: Missing race data is a ubiquitous problem in studies using data from large administrative datasets such as the Veteran Health Administration and other sources. The most common approach to deal with this problem has been analyzing only those records with complete data, Complete Case Analysis (CCA) which requires the assumption of Missing Completely At Random (MCAR) but CCA could lead to biased estimates with inflated standard errors. Objective: To examine the performance of a new imputation approach, Latent Class Multiple Imputation (LCMI), for imputing missing race data and make comparisons with CCA, Multiple Imputation (MI) and Log-Linear Multiple Imputation (LLMI). Design/Participants: To empirically compare LCMI to CCA, MI and LLMI using simulated data and demonstrate their applications using data from a sample of 13,705 veterans with type 2 diabetes among whom.. Read More»
  DOI: 10.4172/2155-6180.1000138

  Abstract PDF