Dealing with missing data in longitudinal studies

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Longitudinal studies – studies in which individuals are followed over periods of many months or years – are of great importance in understanding how aspects of people’s lifestyle or environment influence their risk of disease. When individuals are followed over extended periods it is inevitable that measurements on particular variables are sometimes missing, for example because a measuring device broke down, or a subject did not answer certain questions or did not attend an examination. Missing values make analyses of data from longitudinal studies more complicated, because they can lead to results that are both biased (they differ from the results that would be observed if the missing values were taken into account) and inefficient (they are less precise than they would be if missing values were taken into account). New statistical methods that address these issues have been proposed, and have the potential to decrease bias and increase efficiency in analyses of longitudinal studies. However these methods can be complex and there is currently little practical experience of their use. We are investigating practical issues in applying these methods, in particular a method called multiple imputation, in order to demonstrate the circumstances in which they are useful, develop strategies for choosing models and confirming that they are appropriate, and compare different approaches and software. As well as publishing the results of the study in scientific journals, we will develop guidelines for people who use the methods in the future.