Applied Missing Data Analysis in Health Sciences

 
   Lead Investigator:    Andrew Zhou
   Institution      :    University of Washington
   E-Mail           :    azhou@u.washington.edu
   Proposal ID      :    94


   Publications:


1  Chen B, Zhou A
   A Latent-Variable Marginal Method for Multi-Level Incomplete Binary Data
   Journal Article  Stat Med,31:3211-3222,2012,PMC3631603

   PUBLISHED



2  Chen B, Zhou A
   Doubly Robust Estimates for Binary Longitudinal Data Analysis with Missing Response and Missing Covariates
   Journal Article  Biometrics,67:830-842,2011,PMC3652597

   PUBLISHED



3  Chen B, Zhou XHA
   Non-Homogeneous Markov Process Models with Informative Observation Times, with an Application to Alzheimer's Disease
   Abstract  Alzheimers Dement,7:S366,2011,

   PUBLISHED



4  Chen B , Qin J
   A new estimation with minimum trace of asymptotic covariance matrix for incomplete longitudinal data with a surrogate process
   Journal Article  Stat Med,32:4763-4780,2013,PMC3808493

   PUBLISHED





   Proposal Description:
   We (Drs. Andrew Zhou, Leslie Taylor, and Chuan Zhou) are working on an application-oriented, reader-friendly statistical textbook on missing data analysis with biomedical data. To our knowledge missing data do exist in NACC data, but the amount of missingness, patterns of missingness, mechanisms of missingness and their impact on the analysis and results have not been fully investigated. We plan to conduct secondary analyses based on the Uniform Data Set (UDS), investigating the impact of missing data. With this work we hope to provide recommendations and guidelines on missing data analysis to other researchers who perform quantitative analysis using the NACC data.