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The multiple imputation of the National Medical Expenditure Survey (NMES) involved the use of two new techniques, both having potentially broad applicability. The first is to use distributionally incompatible MCMC (Markov Chain Monte Carlo), but to apply it only partially, to impute the missing values that destroy a monotone pattern, thereby limiting the extent of incompatibility. The second technique is to split the missing data into two parts, one that is much more computationally expensive to impute than the other, and create several imputations of the second part for each of the first part, thereby creating nested multiple imputations with their increased inferential efficiency.

Studies employing within-subjects designs may be compared with those employing between-subjects designs in a variety of ways. We discuss and illustrate the comparisons of variabilities, including within-condition variances and precisions as well as the comparisons of means and of mean differences. Our discussion emphasizes the importance of trying to understand the sources of differences.

Praise for the First Edition of Statistical Analysis with Missing Data ""An important contribution to the applied statistics literature ... I give the book high marks for unifying and making accessible much of the past and current work in this important area.""--William E. Strawderman, Rutgers University ""This book ... provide[s] interesting real-life examples, stimulating end-of-chapter exercises, and up-to-date references. It should be on every applied statistician's bookshelf.""-The Statistician ""The book should be studied in the statistical methods department in every statistical agency.""-

Methods for handling missing data in social science data sets are reviewed. Limitations of common practical approaches, including complete-case analysis, available-case analysis and imputation, are illustrated on a simple missing-data problem with one complete and one incomplete variable. Two more principled approaches, namely maximum likelihood under a model for the data and missing-data mechanism and multiple imputation, are applied to the bivariate problem. General properties of these methods are outlined, and applications to more complex missing-data problems are discussed. The EM algorithm, a convenient method for computing maximum likelihood estimates in missing-data problems, is described and applied to two common models, the multivariate normal model for continuous data and the multinomial model for discrete data. Multiple imputation under explicit or implicit models is recommended as a method that retains the advantages of imputation and overcomes its limitations.

The problem of drawing causal inferences from retrospective case-control studies is considered. A model for causal inference in prospective studies is reviewed and then applied to retrospective studies. The limitations of case-control studies are formulated in terms of the level of causally relevant parameters that can be estimated in such studies. An example using data from a large retrospective study of coffee-drinking and myocardial infarctions is used to illustrate the ideas of the article.

This paper provides empirical evidence about the effect of unearned income on earnings, consumption, and savings. Using an original survey of people playing the lottery in Massachusetts in the mid-1980's, we analyze the effects of the magnitude of lottery prizes on economic behavior. The critical assumption is that among lottery winners the magnitude of the prize is randomly assigned. We find that unearned income reduces labor earnings, with a marginal propensity to consume leisure of approximately 11 percent, with larger effects for individuals between 55 and 65 years old. After receiving about half their prize, individuals saved about 16 percent. (JEL C81, D12, E21, J22, J26)