Title: Relative efficiency of joint-model and full-conditional-specification multiple imputation when models are compatible
Authors: Shaun Seaman - University of Cambridge (United Kingdom) [presenting]
Rachael Hughes - Bristol University (United Kingdom)
Abstract: Fitting a regression model of interest is often complicated by missing data on the variables in that model. Multiple imputation (MI) is commonly used to handle these missing data. Two popular methods of MI are joint model MI and full-conditional-specification (FCS) MI. These are known to yield imputed data with the same asymptotic distribution when the conditional models of FCS are compatible with the joint model. We show that this asymptotic equivalence of imputation distributions does not imply that joint model MI and FCS MI will also yield asymptotically equally efficient inference about the parameters of the model of interest, nor that they will be equally robust to misspecification of the joint model. When the conditional models used by FCS MI are linear, logistic and multinomial regressions, these are compatible with a restricted general location (RGL) joint model. We show that MI using the RGL joint model (RGL MI) can be substantially more asymptotically efficient than FCS MI, but this typically requires very strong associations between variables. When associations are weaker, the efficiency gain is small. Moreover, FCS MI is shown to be potentially much more robust than RGL MI to misspecification of the RGL model when there is substantial missingness in the outcome variable.