Bias and Efficiency Comparison between Multiple Imputation and Available-Case Analysis for Missing Data in Longitudinal Models.

Abstract

In this paper, we compare the performance of available-case analysis (ACA) and several multiple imputation (MI) approaches for handling missing data problems in longitudinal analysis through estimation bias and relative efficiency. When the missingness of covariates depends on observed responses, ACA produces estimation bias, but it is preferred when there are only missing values in longitudinal responses. Multilevel MI methods are not always a solution to longitudinal data analysis. Single-level MI methods, like fully conditional specification (FCS), provide unbiased estimates under a variety of missing data scenarios, and improve efficiency gain in certain scenarios. The general assumption of missing data mechanism is missing at random (MAR). We carry out a systematic synthetic data analysis where missing data exist in longitudinal outcomes or/and covariates under different kinds of missing data generation procedures. The analysis model is a linear mixed-effects model. For each of the missing data scenarios, we give our recommendation (between ACA and a specific MI method) based on theoretical justifications and extensive simulations. In addition, a longitudinal neurodegenerative disease dataset is used as a real case study.