Combining multiple imputation with raking of weights: An efficient and robust approach in the setting of nearly-true models

TL;DR

This paper introduces a combined approach of multiple imputation and raking of weights to achieve an efficient and robust estimator for missing data, especially when models are nearly correct, balancing bias and variance.

Contribution

It proposes a practical method integrating multiple imputation with raking to improve robustness and efficiency in missing data estimation, outperforming standard methods under model misspecification.

Findings

Proposed raking estimator with MI outperforms standard methods under mild model misspecification.

The combined approach maintains robustness while achieving near-optimal efficiency.

Numerical examples demonstrate the method's advantages over traditional estimators.

Abstract

Multiple imputation provides us with efficient estimators in model-based methods for handling missing data under the true model. It is also well-understood that design-based estimators are robust methods that do not require accurately modeling the missing data; however, they can be inefficient. In any applied setting, it is difficult to know whether a missing data model may be good enough to win the bias-efficiency trade-off. Raking of weights is one approach that relies on constructing an auxiliary variable from data observed on the full cohort, which is then used to adjust the weights for the usual Horvitz-Thompson estimator. Computing the optimally efficient raking estimator requires evaluating the expectation of the efficient score given the full cohort data, which is generally infeasible. We demonstrate multiple imputation (MI) as a practical method to compute a raking estimator that will be optimal. We compare this estimator to common parametric and semi-parametric estimators, including standard multiple imputation. We show that while estimators, such as the semi-parametric maximum likelihood and MI estimator, obtain optimal performance under the true model, the proposed raking estimator utilizing MI maintains a better robustness-efficiency trade-off even under mild model misspecification. We also show that the standard raking estimator, without MI, is often competitive with the optimal raking estimator. We demonstrate these properties through several numerical examples and provide a theoretical discussion of conditions for asymptotically superior relative efficiency of the proposed raking estimator.