Tools for using multiple imputation for missing data in Stata

A major analytic challenge in epidemiological studies is the threat to validity and precision of conclusions raised by missing data. It is still commonly accepted practice to analyze data containing missing values by "complete-case" methods, where entire individuals are omitted from the analysis if they have a missing value on any of the variables required for the analysis in question. This approach can lead to biases in conclusions, by excluding individuals in whom patterns of association may be different than among those retained, and at best leads to loss of precision due to the reduction in sample size available for analysis. The method of multiple imputation is gaining popularity as an approach for dealing with missing data. It involves the production of multiple complete datasets based on a statistical model for the missing values given the observed data. Each of the imputed datasets is then analyzed using standard methods, and valid inferences are obtained by combining these estimates appropriately. Given tools for (a) imputing the missing values, and (b) analyzing the multiple imputed datasets, the method offers great flexibility. In this talk I will review currently available tools for task (a), ranging from fully model-based methods provided in software developed by Schafer and now available in packages such as SAS and S-PLUS to more pragmatic but flexible techniques such as the use of chained equations. Stata commands for performing the latter technique have recently been developed by Patrick Royston, and we are working to develop Stata interfaces for some of Schafer's methods. Tools for task (b) have been fairly limited but we have recently published a flexible package of commands in Stata, which allows a wide range of data manipulations as well as combined analyses to be performed on multiple imputed datasets with minimal effort. We have used multiple imputation to address missing data problems in the Victorian Adolescent Health Cohort Study (VAHCS), which began in 1992 with participants aged 15 and has recently completed an 8th wave of data collection, and analyses of data from this study will be used in the talk to illustrate the methods and to highlight outstanding issues, both statistical and computational.