ReviewNeuro-oncology advances
Biostatistical benchmarking of neuro-oncology trials.
Review in Neuro-oncology advances. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Purpose: High-grade gliomas are among the most treatment-resistant cancers, with few therapies improving survival despite hundreds of clinical trials. The biostatistical parameters used to design these trials are critical but uncharacterized in aggregate. Currently, no data-driven benchmarks exist to inform these parameters, including the target effect size. This study therefore aimed to analyze trends in their use across neuro-oncology trials. Methods: We systematically searched PubMed for publications of phase 2 and 3 high-grade glioma or medulloblastoma trials comparing two or more arms with a time-to-event primary endpoint. Key biostatistical parameters were extracted from each study, including trial phase, endpoints, effect size, control arm survival assumptions, Type I error, power, sample size, and accrual time. Results: We analyzed 210 trials published between 1976 and 2025, evaluating 254 primary time-to-event endpoints (overall, OS, or progression-free survival or similar). Survival assumptions and target effect sizes (ie, hazard ratios, HRs) used for trial powering varied substantially. Assumed control arm OS was often lower than observed OS, and the application of type I error rates did not consistently reflect the stated hypothesis directionality. Trials with planned sample sizes below 500 and lower HR targets were less likely to meet accrual goals. Trials generally aimed to complete accrual by 36 months with a median follow up of 24 months. Conclusion: This study provides historical benchmarks for sample size assumptions to support more transparent, data-driven, and context-aware trial design. They may also serve as a resource for feasibility planning, protocol development, and statistical justification in future trials.
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What Socratic holds
Registered trials
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.