Paul Madley?Dowd|Rachael A. Hughes|Kate Tilling|Jon Heron
The proportion of missing data should not be used to guide decisions on multiple imputation
University of Bristol|University of Bristol|University of Bristol|University of Bristol
researchers concerned whether multiple imputation mi complete case analysis used large proportion data missing aimed provide guidance drawing conclusions data large proportion missingnessvia simulations investigated proportion missing data fraction missing information fmi availability auxiliary variables affected mi performance outcome data missing completely random missing random marprovided sufficient auxiliary information available mi beneficial terms bias never detrimental terms efficiency models similar fmi values differing proportions missing data also similar precision effect estimates absence bias fmi better guide efficiency gains using mi proportion missing datawe provide evidence mar data valid mi reduces bias even proportion missingness large advise researchers use fmi guide choice auxiliary variables efficiency gain imputation analyses sensitivity analyses including different imputation models may needed number complete cases small