Missing Data Methods

Paul D. Allison

Virtually all epidemiologic studies suffer from some degree of missing or incomplete data. This means that some of the cases have data missing on some (but not all) of the variables. For example, one patient in a study may have values present for all variables except age. Another may have missing data on blood pressure and years of schooling. Missing data create problems for most statistical methods because they presume that every case has measured values on all variables in whatever model is being estimated. This entry surveys some of the many methods that have been developed to deal with these problems. The most common method for handling missing data is complete case analysis (also known as listwise or casewise deletion). In this method, cases are deleted from the analysis if they have missing data on any of the variables under consideration, thereby using only complete cases. Because of its ...

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