A cautionary case study of approaches to the treatment of missing data

This article presents findings from a case study of different approaches to the treatment of missing data. Simulations based on data from the Los Angeles Mammography Promotion in Churches Program (LAMP) led the authors to the following cautionary conclusions about the treatment of missing data: (1) Automated selection of the imputation model in the use of full Bayesian multiple imputation can lead to unexpected bias in coefficients of substantive models. (2) Under conditions that occur in actual data, casewise deletion can perform less well than we were led to expect by the existing literature. (3) Relatively unsophisticated imputations, such as mean imputation and conditional mean imputation, performed better than the technical literature led us to expect. (4) To underscore points (1), (2), and (3), the article concludes that imputation models are substantive models, and require the same caution with respect to specificity and calculability. The research reported here was partially supported by National Institutes of Health, National Cancer Institute, R01 CA65879 (SAF). We thank Nicholas Wolfinger, Naihua Duan, John Adams, John Fox, and the anonymous referees for their thoughtful comments on earlier drafts. The responsibility for any remaining errors is ours alone. Benjamin Stein was exceptionally helpful in orchestrating the simulations at the labs of UCLA Social Science Computing. Michael Mitchell of the UCLA Academic Technology Services Statistical Consulting Group artfully created Fig. 1 using the Stata graphics language; we are most grateful.