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This simulation study evaluates the effectiveness of multiple imputation (MI)techniques for multilevel data. It compares the performance of traditionalMultiple Imputation by Chained Equations (MICE) with tree-based methods such asChained Random Forests with Predictive Mean Matching and Extreme GradientBoosting. Adapted versions that include dummy variables for cluster membershipare also included for the tree-based methods. Methods are evaluated forcoefficient estimation bias, statistical power, and type I error rates onsimulated hierarchical data with different cluster sizes (25 and 50) and levelsof missingness (10\% and 50\%). Coefficients are estimated using randomintercept and random slope models. The results show that while MICE ispreferred for accurate rejection rates, Extreme Gradient Boosting isadvantageous for reducing bias. Furthermore, the study finds that bias levelsare similar across different cluster sizes, but rejection rates tend to be lessfavorable with fewer clusters (lower power, higher type I error). In addition,the inclusion of cluster dummies in tree-based methods improves estimation forLevel 1 variables, but is less effective for Level 2 variables. When databecome too complex and MICE is too slow, extreme gradient boosting is a goodalternative for hierarchical data.  Keywords: Multiple imputation; multi-level data; MICE; missRanger; mixgb

Adapting tree-based multiple imputation methods for multi-level data? A simulation study