Skip to content
DraconiticResearch
Search Sources API
186,625 studies31 sourcesUpdated 24 min ago
PreprintarXiv licencearXiv

Estimating Treatment Effects with a Single Arm Clinical Trial and Data Summaries from an External Control Group: is it Possible?

Abstract

Randomized controlled trials are crucial for assessing new treatments, but they are often infeasible due to ethical or practical constraints. In these cases, single-arm trials combined with external control data constitute an alternative approach for estimating treatment effects. However, individual patient-level data on external controls are frequently inaccessible due to privacy and economic concerns. Therefore, investigators rely on data summaries published by previous studies of the control therapy. We propose a method for inference on the treatment effect of an experimental therapy using patient-level data from a single-arm trial and a few published data summaries from an external control group. We introduce and study the Interval of Potential Effects (IPE), described as the range of plausible levels of the treatment effect that are compatible with the patient-level data from the experimental treatment and the available summaries on the control group. The width of the IPE quantifies the uncertainty on the treatment effect attributable to the lack of individual patient data from the control group. We demonstrate, through simulations and a case study, how the IPE indicates whether the available summaries are sufficient for accurate estimation of the treatment effect. We also illustrate that IPEs can support decision-making in the early phases of development of new treatments.

Related

  1. Semiparametric inference for a Target Outcome Leveraging Heterogeneous External Summaries of Auxiliary Outcomes
    Yifei Wang, Jiayin Zheng, Christina C. Newton +1
    arXivPreprintCC BY-NC-ND 4.0Full textStatisticsarXiv
  2. Estimating Causal Treatment Effects in Placebo-Controlled Randomized Clinical Trials When High Placebo Response is Anticipated
    Yang Song, Yunzhe Qian, Chanmin Kim +1
    arXivPreprintarXiv licenceFull textStatisticsarXiv
  3. Estimating Average Treatment Effects with Projected Instrumental Variables
    Shuyuan Chen, Yifan Cui
    arXivPreprintarXiv licenceFull textStatisticsarXiv
  4. Imputation is all you need: double robustness, semiparametric efficiency, and automatic covariate balance for estimating the average treatment effect
    Fang Han, Peng Ding
    arXivPreprintarXiv licenceFull textStatisticsarXiv
  5. Estimating Optimal Dynamic Treatment Regimes for Personalized Education: A Tutorial and Applications with Machine Learning
    Chenguang Pan, Yuxuan Li, Thomas Harris +1
    arXivPreprintCC BY 4.0Full textStatisticsarXiv
BibTeX
@misc{saha2026estimating,
  title = {{Estimating Treatment Effects with a Single Arm Clinical Trial and Data Summaries from an External Control Group: is it Possible?}},
  author = {Riddhiman Saha and Lorenzo Trippa},
  howpublished = {arXiv},
  year = {2026},
  month = nov,
  doi = {10.48550/arxiv.2611.05016},
  eprint = {2611.05016},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2611.05016},
}
RIS
TY  - UNPB
TI  - Estimating Treatment Effects with a Single Arm Clinical Trial and Data Summaries from an External Control Group: is it Possible?
AU  - Riddhiman Saha
AU  - Lorenzo Trippa
PY  - 2026
DA  - 2026/11/03
PB  - arXiv
DO  - 10.48550/arxiv.2611.05016
UR  - https://arxiv.org/abs/2611.05016
AB  - Randomized controlled trials are crucial for assessing new treatments, but they are often infeasible due to ethical or practical constraints. In these cases, single-arm trials combined with external control data constitute an alternative approach for estimating treatment effects. However, individual patient-level data on external controls are frequently inaccessible due to privacy and economic concerns. Therefore, investigators rely on data summaries published by previous studies of the control therapy. We propose a method for inference on the treatment effect of an experimental therapy using patient-level data from a single-arm trial and a few published data summaries from an external control group. We introduce and study the Interval of Potential Effects (IPE), described as the range of plausible levels of the treatment effect that are compatible with the patient-level data from the experimental treatment and the available summaries on the control group. The width of the IPE quantifies the uncertainty on the treatment effect attributable to the lack of individual patient data from the control group. We demonstrate, through simulations and a case study, how the IPE indicates whether the available summaries are sufficient for accurate estimation of the treatment effect. We also illustrate that IPEs can support decision-making in the early phases of development of new treatments.
ER  -