Imputation for Repeated Bounded Outcome Data: Statistical and Machine-Learning Approaches

Real-life data are bounded and heavy-tailed variables. Zero-one-inflated beta (ZOIB) regression is used for modelling them. There are no appropriate methods to address the problem of missing data in repeated bounded outcomes. We developed an imputation method using ZOIB (i-ZOIB) and compared its performance with those of the naive and machine-learning methods, using different distribution shapes and settings designed in the simulation study. The performance was measured employing the absolute error (MAE), root-mean-square-error (RMSE) and the unscaled mean bounded relative absolute error (UMBRAE) methods. The results varied depending on the missingness rate and mechanism. The i-ZOIB and the machine-learning ANN, SVR and RF methods showed the best performance.

Automated summary

The study compared the performance of the i-ZOIB imputation method with naive and machine-learning methods in addressing missing data in repeated bounded outcomes, with i-ZOIB and machine-learning methods such as ANN, SVR, and RF showing the best performance depending on the missingness rate and mechanism.