Multiple imputation of missing data in longitudinal health records

Electronic health records are increasingly used for epidemiological and health service research. However, missing data are often an issue when dealing with electronic records. Up to now, various approaches have been used to overcome these issues, including complete case analysis, last observation carried forward, and multiple imputation. In this presentation, we will first highlight the issues of missing data in longitudinal records and provide examples of the limitations of standard methods of multiple imputation. We will then demonstrate the new twofold user-written Stata command that implements the twofold fully conditional specification (FCS) multiple-imputation algorithm in Stata (Nevalainen, Kenward, and Virtanen, 2009. Stat Med. 28: 3657–3669.) In the application of the twofold FCS algorithm, we divide time into equal size time blocks. The algorithm then imputes missing values in the longitudinal data, imputing one time block, and then the next. The defining characteristic is that when one imputes missing values at a particular time block, only measurements at that time block and adjacent time blocks are used. This obviates some of the principal difficulties that are typically encountered when attempting to apply a standard MI approach to imputing such longitudinal data. We illustrate how the twofold FCS MI algorithm works in practice and maximizes the use of data available, even in situations where measurements are only made on a relatively small proportion of individuals in each time block. We discuss some of the strengths and limitations of the twofold FCS MI algorithm and contrast it with existing approaches to imputing longitudinal data. Lastly, we present results demonstrating the potential for gains in efficiency through use of the twofold approach compared with a more conventional “baseline MI†approach.