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Missing data are common in data analyses in biomedical fields, and imputationmethods based on random forests (RF) have become widely accepted, as the RFalgorithm can achieve high accuracy without the need for specification of datadistributions or relationships. However, the predictions from RF do not containinformation about prediction uncertainty, which was unacceptable for multipleimputation. Available RF-based multiple imputation methods tried to do propermultiple imputation either by sampling directly from observations underpredicting nodes without accounting for the prediction error or by makingnormality assumption about the prediction error distribution. In this study, anovel RF-based multiple imputation method was proposed by constructingconditional distributions the empirical distribution of out-of-bag predictionerrors. The proposed method was compared with previous method with parametricassumptions about RF's prediction errors and predictive mean matching based onsimulation studies on data with presence of interaction term. The proposednon-parametric method can deliver valid multiple imputation results. Theaccompanying R package for this study is publicly available.

Multiple imputation using chained random forests: a preliminary study based on the empirical distribution of out-of-bag prediction errors