Biostatistical benchmarking of neuro-oncology trials

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.

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.

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.

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.