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 repeat..