Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Neuro Oncol. 2023 Sep 5;25(9):1658-1671. doi: 10.1093/neuonc/noad036.
Limitations in trial design, accrual, and data reporting impact efficient and reliable drug evaluation in cancer clinical trials. These concerns have been recognized in neuro-oncology but have not been comprehensively evaluated. We conducted a semi-automated survey of adult interventional neuro-oncology trials, examining design, interventions, outcomes, and data availability trends.
Trials were selected programmatically from ClinicalTrials.gov using primary malignant central nervous system tumor classification terms. Regression analyses assessed design and accrual trends; effect size analysis utilized survival rates among trials investigating survival.
Of 3038 reviewed trials, most trials reporting relevant information were nonblinded (92%), single group (65%), nonrandomized (51%), and studied glioblastomas (47%) or other gliomas. Basic design elements were reported by most trials, with reporting increasing over time (OR = 1.24, P < .00001). Trials assessing survival outcomes were estimated to assume large effect sizes of interventions when powering their designs. Forty-two percent of trials were completed; of these, 38% failed to meet their enrollment target, with worse accrual over time (R = -0.94, P < .00001) and for US versus non-US based trials (OR = 0.5, P < .00001). Twenty-eight percent of completed trials reported partial results, with greater reporting for US (34.6%) versus non-US based trials (9.3%, P < .00001). Efficacy signals were detected by 15%-23% of completed trials reporting survival outcomes.
Low randomization rates, underutilization of controls, and overestimation of effect size, particularly pronounced in early-phase trials, impede generalizability of results. Suboptimal designs may be driven by accrual challenges, underscoring the need for cooperative efforts and novel designs. The limited results reporting highlights the need to incentivize data reporting and harmonization.
临床试验设计、入组和数据报告方面的局限性会影响癌症药物评估的效率和可靠性。神经肿瘤学领域已经认识到了这些问题,但尚未进行全面评估。我们对半自动化的成人介入神经肿瘤学试验进行了调查,研究了设计、干预措施、结果和数据可用性趋势。
使用原发性恶性中枢神经系统肿瘤分类术语,从 ClinicalTrials.gov 中选择试验进行程序筛选。回归分析评估了设计和入组趋势;利用研究生存结果的试验中的生存率进行效应量分析。
在 3038 项审查试验中,大多数报告相关信息的试验为非盲法(92%)、单组(65%)、非随机(51%),且研究对象为胶质母细胞瘤(47%)或其他胶质瘤。大多数试验报告了基本设计要素,且随着时间的推移报告量逐渐增加(OR = 1.24,P <.00001)。评估生存结果的试验在设计时估计干预措施的效应量较大。42%的试验已完成;其中,38%未达到入组目标,且随着时间的推移入组情况恶化(R = -0.94,P <.00001),美国试验和非美国试验之间的差异更大(OR = 0.5,P <.00001)。28%的已完成试验报告了部分结果,美国试验(34.6%)的报告率高于非美国试验(9.3%,P <.00001)。有生存结果报告的完成试验中,15%-23%检测到了疗效信号。
低随机化率、对照措施利用不足以及对效应量的高估,尤其是在早期试验中,这些问题限制了研究结果的普遍性。不理想的设计可能是由于入组挑战所致,这突显了合作努力和新设计的必要性。有限的结果报告强调了需要激励数据报告和协调。