One-step Outcome Imputation: An Alternative to Multiple Imputation
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. The one-step imputation estimation procedure is implemented in the `targeted` R package.
Related
- Bayesian multiple imputation for functional data
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- Reference-based multiple imputation for longitudinal bounded discrete outcomes
- Bayesian Multiple Imputation for Functional Data
- Food-System Stress Testing: Prediction Tasks Under Alternative Outcome Definitions
BibTeX
@misc{nordland2026onestep,
title = {{One-step Outcome Imputation: An Alternative to Multiple Imputation}},
author = {Andreas Nordland and Klaus K. Holst and David Redek and Christian B. Pipper and Aske T. Iversen},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2606.07174},
eprint = {2606.07174},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.07174},
}
RIS
TY - UNPB
TI - One-step Outcome Imputation: An Alternative to Multiple Imputation
AU - Andreas Nordland
AU - Klaus K. Holst
AU - David Redek
AU - Christian B. Pipper
AU - Aske T. Iversen
PY - 2026
DA - 2026/09/11
PB - arXiv
DO - 10.48550/arxiv.2606.07174
UR - https://arxiv.org/abs/2606.07174
AB - 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. The one-step imputation estimation procedure is implemented in the `targeted` R package.
ER -