Multiple Imputation When Rate of Change is the Outcome of Interest

Imputing Longitudinal Growth Data in International Pediatric Studies: Does CDC Reference Suffice?

This study investigates a missing value imputation approach for longitudinal growth data in pediatric studies from multiple countries. We analyzed a combined cohort from five natural history studies of type 1 diabetes (T1D) in the US and EU with longitudinal growth measurements for 23,201 subjects. We developed a multiple imputation methodology using LMS parameters of CDC reference data. We measured imputation errors on both combined and individual cohorts using mean absolute percentage error (MAPE) and normalized root-mean-square error (NRMSE). Our results show low imputation errors using CDC reference. Overall height imputation errors were lower than for weight. The largest MAPE for weight and height among all age groups was 4.8% and 1.7%, respectively. When comparing performance between CDC reference and country-specific growth charts, we found no significant differences for height (CDC vs. German: p =0.993, CDC vs. Swedish: p=0.368) and for weight (CDC vs. Swedish: p=0.513) for all ages.

How to Apply Multiple Imputation in Propensity Score Matching with Partially Observed Confounders: A Simulation Study and Practical Recommendations

Propensity score matching (PSM) has been widely used to mitigate confounding in observational studies, although complications arise when the covariates used to estimate the PS are only partially observed. Multiple imputation (MI) is a potential solution for handling missing covariates in the estimation of the PS. However, it is not clear how to best apply MI strategies in the context of PSM. We conducted a simulation study to compare the performances of popular non-MI missing data methods and various MI-based strategies under different missing data mechanisms. We found that commonly applied missing data methods resulted in biased and inefficient estimates, and we observed large variation in performance across MI-based strategies. Based on our findings, we recommend 1) estimating the PS after applying MI to impute missing confounders; 2) conducting PSM within each imputed dataset followed by averaging the treatment effects to arrive at one summarized finding; 3) a bootstrapped-based variance to account for uncertainty of PS estimation, matching, and imputation; and 4) inclusion of key auxiliary variables in the imputation model.