One-step Outcome Imputation: An Alternative to Multiple Imputation

2026-06-05

AI Summary

In randomized controlled trials, methods such as reference-based multiple imputation often yield invalid standard error estimates due to violations of Rubin’s rules. This work proposes a one-step estimation framework that directly leverages the influence function of the target treatment effect’s imputation model to construct an asymptotically efficient estimator, thereby circumventing the need for multiple imputation. The approach accommodates various settings—including reference-based imputation and imputation under intercurrent event dependence—while ensuring consistency of the treatment effect estimate and achieving asymptotic validity of standard errors. By doing so, it substantially enhances inferential reliability and reduces computational burden compared to conventional multiple imputation strategies.

Abstract

Missing outcomes in randomized controlled trials are often handled by multiple imputation (MI). Rubin's rules are routinely used to estimate standard errors but can fail to provide valid standard error estimates for some commonly used procedures, such as reference-based imputation. We propose a one-step alternative by explicitly targeting the treatment effect implied by a given imputation model and constructing an efficient one-step estimator for that treatment effect via its influence function. Unlike Rubin's rules, this approach yields asymptotically valid inference. Moreover, the proposed method circumvents the stochastic component and computational burden of MI. We illustrate the approach with examples spanning a range of imputation models, including reference-based imputation and intercurrent-event-dependent imputation.

Problem

• missing outcomes
• multiple imputation
• Rubin's rules
• standard error estimation
• randomized controlled trials

Innovation

• one-step estimator
• missing data
• randomized controlled trials
• reference-based imputation
• influence function