---
title: "Model‑Robust G‑Computation for Hybrid Control Studies Without Assuming Exchangeability"
id: "pubmed-42802529"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42802529"
content_type: "clinical_feed_article"
specialty: "Infectious Disease"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42802529/"
doi: "10.1002/sim.70754"
published_at: "2026-10-01T00:00:00.000Z"
evidence_level: "Journal Article"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Model‑Robust G‑Computation for Hybrid Control Studies Without Assuming Exchangeability
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42802529
- **Specialty:** [Infectious Disease](https://medichelpline.com/clinical-feed/infectious-disease.md)
- **Primary Source:** PubMed / NCBI
- **Source URL:** [Original Journal Publication](https://pubmed.ncbi.nlm.nih.gov/42802529/)
- **DOI:** [10.1002/sim.70754](https://doi.org/10.1002%2Fsim.70754)
- **Published At:** 2026-10-01T00:00:00.000Z
- **Evidence Rating:** Journal Article
## Executive GIST (TL;DR)
- Hybrid control designs combine an internal randomized control group with **external control** data from prior trials or real‑world sources to improve efficiency but risk bias. - Bias may be reduced by adjusting for baseline covariates related to the control outcome; many methods assume exchangeability after conditioning on measured covariates. - Violations of the **exchangeability** assumption can be handled using a **g-computation** approach with variable selection under a correctly specified outcome regression model. - The authors identify a particular variant of g-computation that remains valid when the outcome regression is misspecified, yielding a **model-robust g-computation** method. - The proposed procedure is described as simple, easy to implement, consistent, and asymptotically normal under minimal assumptions while exploiting similarities between internal and external controls to improve efficiency. - The method incorporates ideas related to variable selection (keywords include **adaptive lasso**) and covariate adjustment to mitigate bias from nonexchangeability. - Performance of the approach is assessed in a simulation study; it is also illustrated using real data from **HIV** treatment trials. Specific simulation results and numerical performance metrics were not reported in the abstract. - The article situates the method within regulatory and methodological contexts concerned with externally controlled trials and covariate adjustment for randomized clinical trials. - Keywords highlighted by the authors include adaptive lasso, covariate adjustment, external control, g‑computation, model misspecification, and outcome regression.
## Clinical Analysis & Structured Key Points
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Affiliations Expand ### Affiliations * 1 Biostatistics Innovation Group, Gilead Sciences, Foster City, California, USA. * 2 State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China. * PMID: **42802529** * DOI: [ 10.1002/sim.70754 ](https://doi.org/10.1002/sim.70754) Item in Clipboard # A Model-Robust G-Computation Method for Analyzing Hybrid Control Studies Without Assuming Exchangeability Zhiwei Zhang et al. Stat Med. 2026 Oct. Show details Display options Display options Format Abstract PubMed PMID Stat Med Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Stat+Med%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Stat+Med%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42802529/) . 2026 Oct;45(23-24):e70754. doi: 10.1002/sim.70754. ### Authors [Zhiwei Zhang](https://pubmed.ncbi.nlm.nih.gov/?term=Zhang+Z&cauthor_id=42802529)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42802529/#short-view-affiliation-1 "Biostatistics Innovation Group, Gilead Sciences, Foster City, California, USA."), [Peisong Han](https://pubmed.ncbi.nlm.nih.gov/?term=Han+P&cauthor_id=42802529)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42802529/#short-view-affiliation-1 "Biostatistics Innovation Group, Gilead Sciences, Foster City, California, USA."), [Wei Zhang](https://pubmed.ncbi.nlm.nih.gov/?term=Zhang+W&cauthor_id=42802529)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42802529/#short-view-affiliation-2 "State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.") ### Affiliations * 1 Biostatistics Innovation Group, Gilead Sciences, Foster City, California, USA. * 2 State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China. * PMID: **42802529** * DOI: [ 10.1002/sim.70754 ](https://doi.org/10.1002/sim.70754) Item in Clipboard Cite Display options Display options Format Abstract PubMed PMID Linked Discoveries ## Abstract There is growing interest in a hybrid control design for treatment evaluation, where a randomized controlled trial is augmented with external control data from a previous trial or a real-world data source. The hybrid control design has the potential to improve efficiency but also carries the risk of introducing bias. The potential bias in a hybrid control study can be mitigated by adjusting for baseline covariates that are related to the control outcome. Existing methods that serve this purpose commonly assume that the internal and external control outcomes are exchangeable upon conditioning on a set of measured covariates. Possible violations of the exchangeability assumption can be addressed using a g-computation method with variable selection under a correctly specified outcome regression model. In this article, we point out that a particular version of this g-computation method is protected against misspecification of the outcome regression model. This observation leads to a model-robust g-computation method that is remarkably simple and easy to implement, consistent and asymptotically normal under minimal assumptions, and able to improve efficiency by exploiting similarities between the internal and external control groups. The method is evaluated in a simulation study and illustrated using real data from HIV treatment trials. **Keywords:** adaptive lasso; covariate adjustment; external control; g‐computation; model misspecification; outcome regression. © 2026 John Wiley & Sons Ltd. [PubMed Disclaimer](https://pubmed.ncbi.nlm.nih.gov/disclaimer/) ## References 1. 1. P. Massicotte, J. A. Julian, M. Gent, et al., “An Open‐Label Randomized Controlled Trial of Low Molecular Weight Heparin Compared to Heparin and Coumadin for the Treatment of Venous Thromboembolic Events in Children: The REVIVE Trial,” Thrombosis Research 109 (2003): 85–92. 2. 1. M. C. Jansen‐Van Der Weide, C. M. Gaasterland, K. C. Roes, et al., “Rare Disease Registries: Potential Applications Towards Impact on Development of New Drug Treatments,” Orphanet Journal of Rare Diseases 13 (2018): 1–11. 3. 1. Food and Drug Administration, “Guidance for Industry: Considerations for the Design and Conduct of Externally Controlled Trials for Drug and Biological Products,” (2023), . 4. 1. J. G. Ibrahim and M. H. Chen, “Power Prior Distributions for Regression Models,” Statistical Science 15 (2000): 46–60. 5. 1. B. Neuenschwander, G. Capkun‐Niggli, M. Branson, and D. J. Spiegelhalter, “Summarizing Historical Information on Controls in Clinical Trials,” Clinical Trials 7 (2010): 5–18. 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