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.