Imputing missing repeated measures data: how should we proceed?

Abstract

OBJECTIVE: This paper compares six missing data methods that can be used for carrying out statistical tests on repeated measures data: listwise deletion, last value carried forward (LVCF), standardized score imputation, regression and two versions of a closest match method. METHOD: The efficacy of each was investigated under a variety of sample sizes and with differing levels of missingness. Randomly selected samples from a dataset (n = 804) were used to compare the methods using t-tests. Efficacy was defined as the closeness of the estimated t-values to the true t-values from the complete dataset. RESULTS: The results suggest a reliable and efficacious basis for imputation method for repeated measures data is to substitute a missing datum with a value from another individual who has the closest scores on the same variable measured at other timepoints, or the average value of four individuals who have the closest scores on the same variable at other timepoints. The LVCF and standardized score methods performed relatively poorly, which is of concern since these are often recommended. Listwise deletion was also an inefficient missing data method. CONCLUSIONS: Researchers should consider using closest match missing data imputation. Since listwise deletion performed poorly, is widely reported and is the default method in many statistical software packages, the findings have broad implications.