Item-Level Imputation of Missing K10 Data in Unconditional Cash Transfer Trials Using XGBoost

1717 

Contributed Abstract 

Poster 

Nidhi Tandon (1)

(1) N/A, N/A

Nidhi Tandon  
N/A

Nidhi Tandon  
N/A

Traditional imputation methods for psychological scales often focus on aggregate scores, potentially obscuring item-level response patterns. This study addresses the challenge of item- level missingness by introducing a multiple imputation framework that preserves the Kessler-10 (K10) scale's internal structure in a longitudinal setting. We analyzed item responses from the K10 across six waves in a three-arm RCT (n = 878) and, departing from conventional total-score imputation, employed XGBoost with 10 iterations to impute missing values at the individual item level. Despite missing data ranging from 2.5% to 34.4%, our approach yielded robust estimates with model error averaging about 20% of natural item variability. This method enhances the analysis of psychological assessment data by capturing item-specific variability, ultimately improving data integrity in mental health research.

Multiple Imputation by Chained Equation (MICE)|Item-level imputation|XGBoost| |Randomized controlled trial (RCT)|

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