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Fox et al. (1998) carried out a logistic regression analysis with discrete covariates in which one of the covariates was missing for a substantial percentage of respondents. The missing data problem was addressed using the “approximate Bayesian bootstrap.” We return to this missing data problem to provide a form of case study. Using the Fox et al. (1998) data for expository purposes we carry out a comparative analysis of eight of the most commonly used techniques for dealing with missing data. We then report on two sets of simulations based on the original data | What Should We Do About Missing Data A Case Study Using Logistic Regression with Missing Data on a Single Covariate Christopher Paul William M. Mason Daniel McCaffrey Sarah A. Fox CCPR-028-03 October 2003 California Center for Population Research On-Line Working Paper Series What Should We Do About Missing Data A Case Study Using Logistic Regression with Missing Data on a Single Covariate Christopher Paula William M. Masonb Daniel McCaffreyc and Sarah A. Foxd Revision date 24 October 2003 File name miss_pap_final_24oct03.doc a RAND cpaul@rand.org b Department of Sociology and California Center for Population Research University of California-Los Angeles masonwm@ucla.edu c RAND Daniel_McCaffrey@rand.org d Department of Medicine Division of General Internal Medicine and Health Services Research University of California-Los Angeles sfox@mednet.ucla.edu The research reported here was partially supported by National Institutes of Health National Cancer Institute R01 CA65879 SAF . We thank Nicholas Wolfinger Naihua Duan and John Adams for comments on an earlier draft. What should we do about missing data ABSTRACT Fox et al. 1998 carried out a logistic regression analysis with discrete covariates in which one of the covariates was missing for a substantial percentage of respondents. The missing data problem was addressed using the approximate Bayesian bootstrap. We return to this missing data problem to provide a form of case study. Using the Fox et al. 1998 data for expository purposes we carry out a comparative analysis of eight of the most commonly used techniques for dealing with missing data. We then report on two sets of simulations based on the original data. These suggest for patterns of missingness we consider realistic that case deletion and weighted case deletion are inferior techniques and that common simple alternatives are better. In addition the simulations do not affirm the theoretical superiority of Bayesian Multiple Imputation. The apparent explanation .
