Evidence map›Paper›PMID 40696104›Full record

ArticleTherapeutic innovation & regulatory science2025

Treatment Comparison for a Single Arm Study Utilizing External Control: Performing Inference when Imputing Potential Outcomes.

Wei-Chen Chen, Nelson Lu, Chenguang Wang, Yunling Xu

Abstract read
PubMed Publisher
In one paragraph

Article in Therapeutic innovation & regulatory science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Wei-Chen ChenCenter for Devices and Radiological Health, Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA.
Nelson LuCenter for Devices and Radiological Health, Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA. Nelson.Lu@fda.hhs.gov.
Chenguang WangBiostatistics & Data Management, Regeneron Pharmaceuticals Inc, 777 Old Saw Mill River Rd, Tarrytown, NY, 10591, USA.
Yunling XuCenter for Devices and Radiological Health, Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Non-randomized comparative studies are often used to compare treatment effects between an investigational product and a control when randomization is not feasible or difficult in practice. A typical situation is that the product is investigated in a single-arm study, and the control data are collected in an external data source. For such a situation, we propose an alternative approach to draw inference on the treatment effect difference. First, a potential outcome model (POM) for the outcome under control treatment is built based on the external control data source. Next, the POM is utilized to impute outcomes of subjects in the single-arm study as if they were treated with the control treatment. Then the inference on the treatment effect difference can be made by comparing imputed outcomes (for the control) and observed outcomes (for the investigational product). The main purpose of this paper is to provide a proof of concept regarding how to perform inference on the treatment effect between the investigational product and the control under this scenario. We illustrate our approach by assuming the endpoint to follow a normal distribution and the POM to be a linear regression model.

Indexed as

Research DesignHumansLinear ModelsModels, StatisticalTreatment OutcomeExternal controlPotential outcomePotential outcome model (POM)Single-arm studyTreatment comparison

Identifiers

PMID40696104

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.