65: Light-GBM-based Multiple Imputation Technique in Meta-regression to Handle Missing Data.

Music City Center 

Meta-analysts within the social sciences face challenges when encountering missing covariates in meta-regression that can skew statistical inferences. In this study, we investigated the effectiveness of Light Gradient Boosted Methods (Light-GBM) for handling missing data, juxtaposed against standard multiple imputation methods, such as Predictive Mean Matching (PMM). Through a simulation study, we assessed the performance of these methods by measuring bias and precision in scenarios with varying degrees of missingness (5%, 15%, and 30%) and different missing data mechanisms (MCAR, MAR, and MNAR). The findings revealed that while multiple imputation methods could provide accurate estimates in meta-regression, their efficacy varies with higher rates of missingness. LightGBM has shown consistent performance, minimal bias, and stable error ratios across all missing data scenarios, making it practical for multilevel meta-regression. Applying these machine learning techniques in meta-analysis marks a significant methodological advancement, offering a more robust framework for researchers confronting statistical challenges in systematic reviews.

Machine-Learning

Missing Data

LightGBM

Meta Analysis

Meta-Regression 

Business Analytics/Statistics Education Interest Group