Data fusion causal effect estimation for joint outcomes with missing data and correlated components

Music City Center 

Introduction: Data fusion to generalize health economic data from RCTs is a promising approach to inform healthcare policymaking. Recent research comparing 7 estimators found that the augmented calibration weighting (ACW) estimator is consistent and precise even under model misspecification and strong sampling bias (Colnet et al. 2024). However, its performance in estimating ratio statistics (eg. incremental cost effectiveness ratio) used in health economic studies has not been explored, particularly in settings of missingness and correlated outcome components.
Methods: We assess Colnet estimators for ratio statistics under varying missingness mechanisms and correlation structures. Simulated observational (N=49000) and weakly shifted RCT (N=1000) datasets were resampled and estimators calculated across 100 iterations.
Results: Estimator variance for ratio statistics is sensitive to correlation of components. The ACW, AIPSW, and g-formula estimators are consistent and precise under NMAR missingness and correlation (MSE < 0.05; SV <0.01).
Discussion: ACW's robustness for joint outcomes with correlated components and NMAR missingness supports its use in health economic analysis.

Data fusion

Causal inference

Health economic evaluation

Missing data

Incremental cost effectiveness ratio

Joint outcomes 

Health Policy Statistics Section