Evaluation of supplemental samples in longitudinal research with non-normal missing data

Abstract

Missing data is a commonly encountered problem in longitudinal research. Methodological articles provide advice on ways to handle missing data at the analysis stage, however, there is less guidance for researchers who wish to use supplemental samples (i.e., the addition of new participants to the original sample after missing data appear at the second or later measurement occasions) to handle attrition. The purpose of this study is to evaluate the effects of using supplemental samples when analyzing longitudinal data that are non-normally distributed. We distinguish between two supplemental approaches: a refreshment approach where researchers select additional participants using the same criteria as the initial participants (i.e., random selection from the population of interest) and a replacement approach where researchers identify auxiliary variables that explain missingness and select new participants based on those attributes. Overall, simulation results suggest that the addition of refreshment samples, but not replacement samples, is an effective way to respond to attrition in longitudinal research. Indeed, use of refreshment samples may reduce bias of parameter estimates and increase efficiency and statistical power, whereas use of replacement samples results in biased parameter estimates. Our findings may be utilized by researchers considering using supplemental samples and provide guidance for selecting an appropriate supplemental sample approach.