title: Tuning multiple imputation by predictive mean matching and local residual draws.
abstract: BACKGROUND
  Multiple imputation is a commonly used method for handling incomplete covariates as it can provide valid inference when data are missing at random. This depends on being able to correctly specify the parametric model used to impute missing values, which may be difficult in many realistic settings. Imputation by predictive mean matching (PMM) borrows an observed value from a donor with a similar predictive mean; imputation by local residual draws (LRD) instead borrows the donor's residual. Both methods relax some assumptions of parametric imputation, promising greater robustness when the imputation model is misspecified.
  
  
  METHODS
  We review development of PMM and LRD and outline the various forms available, and aim to clarify some choices about how and when they should be used. We compare performance to fully parametric imputation in simulation studies, first when the imputation model is correctly specified and then when it is misspecified.
  
  
  RESULTS
  In using PMM or LRD we strongly caution against using a single donor, the default value in some implementations, and instead advocate sampling from a pool of around 10 donors. We also clarify which matching metric is best. Among the current MI software there are several poor implementations.
  
  
  CONCLUSIONS
  PMM and LRD may have a role for imputing covariates (i) which are not strongly associated with outcome, and (ii) when the imputation model is thought to be slightly but not grossly misspecified. Researchers should spend efforts on specifying the imputation model correctly, rather than expecting predictive mean matching or local residual draws to do the work.
authors.affiliation: Hub for Trials Methodology Research, MRC Clinical Trials Unit at UCL, Aviation House, 125 Kingsway, WC2B 6NH, London, UK. tim.morris@ucl.ac.uk.
authors.forename: Tim P
authors.initials: TP
authors.lastname: Morris
authors.forename: Ian R
authors.initials: IR
authors.lastname: White
authors.forename: Patrick
authors.initials: P
authors.lastname: Royston
mesh_terms: D000418:Albumins / Q000032:analysis*; D035843:Biomedical Research / Q000379:methods; D000077216:Carcinoma, Ovarian Epithelial; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D009375:Neoplasms, Glandular and Epithelial / Q000097:blood* / Q000401:mortality*; D010051:Ovarian Neoplasms / Q000097:blood* / Q000401:mortality*; D012709:Serum Albumin / Q000032:analysis*
chemical_list: D000418:Albumins; D012709:Serum Albumin