Trial reportJournal of the National Cancer Institute2023
Augmenting randomized clinical trial data with historical control data: Precision medicine applications.
Trial report in Journal of the National Cancer Institute, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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.
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.
Who cites it
11 citing papers in PubMed.
- Treatment Effect Reanalysis of the Randomized Individual Screening Trial of Innovative Glioblastoma Therapy in Newly Diagnosed Glioblastoma With External Control Data.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2026Trial
- Combining external placebo-arm data in a prevention trial: effects of cocoa extract supplementation on cardiovascular disease.European journal of epidemiology · 2026Article
- On the use of external controls in clinical trials.Journal of the National Cancer Institute. Monographs · 2025Article
- AUGMENTed Real-World Data Enhances Comparative Efficacy Between Once-Weekly Insulin Icodec with Dosing Guide App Versus Once-Daily Insulin Glargine U300 in Insulin-Naive Type 2 Diabetes.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2025Article
- Two-to-One Randomization: Rarely Advisable.JCO oncology practice · 2024Article
- The challenges of integrating diverse data sources: A case study in major depression.Health services & outcomes research methodology · 2024Article
- Clarifying Causal Effects of Interest and Underlying Assumptions in Randomized and Nonrandomized Clinical Trials in Oncology Using Directed Acyclic Graphs and Single-World Intervention Graphs.JCO clinical cancer informatics · 2024Article
- Leveraging external control data in the design and analysis of neuro-oncology trials: Pearls and perils.Neuro-oncology · 2024Review
- An interpretable framework to identify responsive subgroups from clinical trials regarding treatment effects: Application to treatment of intracerebral hemorrhage.PLOS digital health · 2024Article
- A standardized metric to enhance clinical trial design and outcome interpretation in type 1 diabetes.Nature communications · 2023Article
- Weighing evidence: robustness vs quantity.Journal of the National Cancer Institute · 2023Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As precision medicine becomes more precise, the sizes of the molecularly targeted subpopulations become increasingly smaller. This can make it challenging to conduct randomized clinical trials of the targeted therapies in a timely manner. To help with this problem of a small patient subpopulation, a study design that is frequently proposed is to conduct a small randomized clinical trial (RCT) with the intent of augmenting the RCT control arm data with historical data from a set of patients who have received the control treatment outside the RCT (historical control data). In particular, strategies have been developed that compare the treatment outcomes across the cohorts of patients treated with the standard (control) treatment to guide the use of the historical data in the analysis; this can lessen the potential well-known biases of using historical controls without any randomization. Using some simple examples and completed studies, we demonstrate in this commentary that these strategies are unlikely to be useful in precision medicine applications.
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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 Socratic graph.