ArticleNeuro-oncology2025
Identifying appropriate external control datasets in support of future glioblastoma clinical trials leveraging external data.
Article in Neuro-oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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
2 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
- Assessing time-trend bias in glioblastoma prognosis over 2 decades of clinical trials.Neuro-oncology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundRecent interest in leveraging external data for clinical trial design and analysis in glioblastoma has raised questions on the identification of appropriate data to use as external controls for future trials. We perform a comprehensive analysis assessing candidate sources of external data and comparing clinical trial and real-world datasets in newly diagnosed glioblastoma.
methodsIndividual patient-level data (PLD) from several clinical trials, a large academic institutional database and a registry (National Cancer Database) were used for analysis of patients receiving standard of care radiation with concurrent and adjuvant temozolomide. Data summaries from randomized trials 2012-2022 were analyzed to account for trials without available PLD. Multivariable modeling was employed to compare survival across datasets.
resultsIn total, 8 datasets with PLD for 3061 patients with newly diagnosed glioblastoma treated with standard chemoradiation were analyzed. Patients on trials were younger (age < 60:64% vs. 48%, p < 0.001) and had higher KPS (KPS≥90:58% vs. 48%, P < .001) compared to non-trial patients. Patients in clinical trials exhibited inferior survival relative to non-trial patients (HR 1.30,95%CI 1.13-1.48, P < .001) after adjustment for age, sex, KPS, extent of resection and MGMT methylation status. In assessment of data summaries of 19 randomized trials, there was no detectable time-trend toward improved outcomes 2012-2022.
conclusionsIn newly diagnosed glioblastoma patients treated with standard of care chemoradiation, there were significant differences between trial datasets and real-world datasets but no evidence of a trial effect benefit from trial participation. After adjustment of relevant covariates, there was no evidence of temporal drift of improved survival over the last decade.
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Registered trials
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