MISSING DATA IN LONGITUDINAL STUDIES OF AGING: THE GOOD, THE BAD, AND THE UGLY

Abstract

In longitudinal studies of aging, missing data are inevitable. In the literature on the association between grip strength and survival time in older persons, the methods used to deal with missing values are generally not even mentioned. The default in standard statistical software is listwise deletion - removing all observations with any missing data. Missing data have been classified into three types: missing completely at random (the good), missing at random (the bad) and missing not at random (the ugly). Depending on the type of missing data, listwise deletion may produce biased results.The objectives of this presentation are: 1) To identify different types of missing data that may arise within a longitudinal study of aging and 2) To assess the estimates of the association between grip strength and mortality that result from applying different methods for handling missing data.Using data from the Cardiovascular Health Study, a longitudinal study of 5,201 older persons, we: 1) examined the types of missing data; 2) investigated the association between grip strength and survival time, applying three common methods of handling missing data: listwise deletion, last value carried forward (LOCF), and multiple imputation (MI). We identified all three types of missing data in the CHS. Compared to the method of listwise deletion, estimates of the association between grip strength and survival were weaker when applying LOCF, and stronger with MI. Methods of handling missing data in longitudinal studies of aging should be considered carefully in order to limit the potential for biased results.