Recursive partitioning on incomplete data using surrogate decisions and multiple imputation

The occurrence of missing data is a major problem in statistical data analysis. All scientific fields and data of all kinds and size are touched by this problem. There is a number of ad-hoc solutions which unfortunately lead to a loss of power, biased inference, underestimation of variability and distorted relationships between variables. A more promising approach of rising popularity is multiple imputation by chained equations (MICE) also known as imputation by full conditional specification (FCS). Alternatives to imputation are given by methods with built-in procedures. These include recursive partitioning by classification and regression trees as well as corresponding Random Forests. However there is only few literature comparing the two approaches. Existing evaluations often lack generalizability due to restrictions on data structure and simulation schemes. The application of both methods to several kinds of data and different simulation settings is meant to improve and extend the comparative analyses. Classification and regression studies are examined. Recursive partitioning is executed by two popular tree and one Random Forest implementation. Findings show that multiple imputation produces ambiguous performance results for both, simulated and real life data. Using surrogates instead is a fast and simple way to achieve performances which are only negligible worse and in many cases even superior.