Addressing Missing Data in Clinical Trials With a Hybrid Control Arm.
- 1Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA.
- 2PD Data Science and Analytics, Genentech, South San Francisco, California, USA.
Abstract
Randomized trials have long been the standard for evaluating experimental treatments in medical and pharmacological studies, but the associated time and cost have spurred research into designs that augment randomized studies with data from external sources. Hybrid control arms offer the promise of improved precision-at the possible expense of bias-by forming a control arm from patients randomized to control along with historical controls or patients found in a registry. Although recent reviews have published guidance on minimizing and addressing missing data in clinical trials, the issue has received little attention in the context of trials with a hybrid control arm. We propose novel treatment effect estimators that handle missing outcomes in both the trial and external control datasets, even when the missingness patterns differ between them. These estimators are built from augmented weighted estimating equations and are doubly robust when outcomes are missing at random (MAR). In addition, we introduce a sensitivity analysis using a pattern-mixture model to assess robustness of the effect estimates under violations of the MAR assumption. We compare these estimators to a popular parametric modeling technique through simulation and an application to a real-world trial.
Related
- Comparison of parametric versus machine-learning multiple imputation in clinical trials with missing continuous outcomes.
- Bayesian Methods Incorporating Baseline Covariates for Borrowing Historical Control Data in Randomized Clinical Trials: A Neutral Comparison Study.
- Hybrid Federated Learning for Multimodal Fusion under Missing Modalities and Site Heterogeneity
- Cluster-aware imputation: a hybrid XGBoost-based model for missing data
- Multivariate Analysis of Data From Crossover Clinical Trials
BibTeX
@article{wasserman2026addressing,
title = {{Addressing Missing Data in Clinical Trials With a Hybrid Control Arm.}},
author = {Joshua Wasserman and Herbert Pang and Jiawen Zhu},
journal = {Statistics in medicine},
year = {2026},
month = oct,
doi = {10.1002/sim.70670},
url = {https://europepmc.org/article/MED/42914162},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Addressing Missing Data in Clinical Trials With a Hybrid Control Arm.
AU - Joshua Wasserman
AU - Herbert Pang
AU - Jiawen Zhu
PY - 2026
DA - 2026/10/30
JO - Statistics in medicine
DO - 10.1002/sim.70670
UR - https://europepmc.org/article/MED/42914162
AB - Randomized trials have long been the standard for evaluating experimental treatments in medical and pharmacological studies, but the associated time and cost have spurred research into designs that augment randomized studies with data from external sources. Hybrid control arms offer the promise of improved precision-at the possible expense of bias-by forming a control arm from patients randomized to control along with historical controls or patients found in a registry. Although recent reviews have published guidance on minimizing and addressing missing data in clinical trials, the issue has received little attention in the context of trials with a hybrid control arm. We propose novel treatment effect estimators that handle missing outcomes in both the trial and external control datasets, even when the missingness patterns differ between them. These estimators are built from augmented weighted estimating equations and are doubly robust when outcomes are missing at random (MAR). In addition, we introduce a sensitivity analysis using a pattern-mixture model to assess robustness of the effect estimates under violations of the MAR assumption. We compare these estimators to a popular parametric modeling technique through simulation and an application to a real-world trial.
SN - 0277-6715
SN - 1097-0258
ER -