The proportion of missing data should not be used to guide decisions on multiple imputation

TLDR

The study aims to guide researchers on using multiple imputation versus complete case analysis when missingness is large, emphasizing the role of the fraction of missing information and auxiliary variables. The authors conducted simulation studies under MCAR and MAR scenarios, varying missingness proportion, FMI, and auxiliary variable availability to assess MI performance. Multiple imputation consistently reduces bias and is never less efficient, with the fraction of missing information better predicting efficiency gains than the proportion of missing data.

Abstract

ObjectivesResearchers are concerned whether multiple imputation (MI) or complete case analysis should be used when a large proportion of data are missing. We aimed to provide guidance for drawing conclusions from data with a large proportion of missingness.Study Design and SettingVia simulations, we investigated how the proportion of missing data, the fraction of missing information (FMI), and availability of auxiliary variables affected MI performance. Outcome data were missing completely at random or missing at random (MAR).ResultsProvided sufficient auxiliary information was available; MI was beneficial in terms of bias and never detrimental in terms of efficiency. Models with similar FMI values, but differing proportions of missing data, also had similar precision for effect estimates. In the absence of bias, the FMI was a better guide to the efficiency gains using MI than the proportion of missing data.ConclusionWe provide evidence that for MAR data, valid MI reduces bias even when the proportion of missingness is large. We advise researchers to use FMI to guide choice of auxiliary variables for efficiency gain in imputation analyses, and that sensitivity analyses including different imputation models may be needed if the number of complete cases is small.