Department of Medical Oncology, National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany
Department of Personalized Oncology, University Hospital Mannheim, Heidelberg University, Mannheim, Germany.
BMJ Evid Based Med. 2024 Sep 20;29(5):333-341. doi: 10.1136/bmjebm-2023-112544.
OBJECTIVES: This study aims to analyse the association between clinical trial design and treatment effects for cancer drugs with US Food and Drug Administration (FDA) approval. DESIGN: Cross-sectional study and meta-analysis. SETTING: Data from Drugs@FDA, FDA labels, ClincialTrials.gov and the Global Burden of Disease study. PARTICIPANTS: Pivotal trials for 170 drugs with FDA approval across 437 cancer indications between 2000 and 2022. MAIN OUTCOME MEASURES: Treatment effects were measured in HRs for overall survival (OS) and progression-free survival (PFS), and in relative risk for tumour response. Random-effects meta-analyses and meta-regressions explored the association between treatment effect estimates and clinical trial design for randomised controlled trials (RCTs) and single-arm trials. RESULTS: Across RCTs, greater effect estimates were observed in smaller trials for OS (ß=0.06, p<0.001), PFS (ß=0.15, p<0.001) and tumour response (ß=-3.61, p<0.001). Effect estimates were larger in shorter trials for OS (ß=0.08, p<0.001) and PFS (ß=0.09, p=0.002). OS (ß=0.04, p=0.006), PFS (ß=0.10, p<0.001) and tumour response (ß=-2.91, p=0.004) outcomes were greater in trials with fewer centres. HRs for PFS (0.54 vs 0.62, p=0.011) were lower in trials testing the new drug to an inactive (placebo/no treatment) rather than an active comparator. The analysed efficacy population (intention-to-treat, per-protocol, or as-treated) was not consistently associated with treatment effects. Results were consistent for single-arm trials and in multivariable analyses. CONCLUSIONS: Pivotal trial design is significantly associated with measured treatment effects. Particularly small, short, single-centre trials testing a new drug compared with an inactive rather than an active comparator could overstate treatment outcomes. Future studies should verify results in unsuccessful trials, adjust for further confounders and examine other therapeutic areas. The FDA, manufacturers and trialists must strive to conduct robust clinical trials with a low risk of bias.
目的：本研究旨在分析美国食品和药物管理局（FDA）批准的癌症药物临床试验设计与治疗效果之间的关系。
设计：横断面研究和荟萃分析。
设置：数据来自 Drugs@FDA、FDA 标签、ClincialTrials.gov 和全球疾病负担研究。
参与者：2000 年至 2022 年间，437 种癌症适应症的 170 种药物的关键试验。
主要观察指标：总生存（OS）和无进展生存（PFS）的治疗效果用 HR 衡量，肿瘤反应的治疗效果用相对风险衡量。随机效应荟萃分析和荟萃回归探讨了随机对照试验（RCT）和单臂试验的治疗效果估计与临床试验设计之间的关系。
结果：在 RCT 中，OS（β=0.06，p<0.001）、PFS（β=0.15，p<0.001）和肿瘤反应（β=-3.61，p<0.001）的较小试验中观察到更大的效果估计。OS（β=0.08，p<0.001）和 PFS（β=0.09，p=0.002）的较短试验中效果估计更大。OS（β=0.04，p=0.006）、PFS（β=0.10，p<0.001）和肿瘤反应（β=-2.91，p=0.004）的结果在中心较少的试验中更大。与安慰剂/无治疗相比，新药物测试为无效（安慰剂/无治疗）而不是活性对照的试验中，PFS 的 HR 更低（0.54 与 0.62，p=0.011）。分析的疗效人群（意向治疗、方案治疗或实际治疗）与治疗效果不一致。单臂试验和多变量分析的结果一致。
结论：关键试验设计与测量的治疗效果显著相关。与安慰剂/无治疗相比，新药物测试为无效（安慰剂/无治疗）而不是活性对照的特别小、短、单中心试验可能夸大治疗结果。未来的研究应该在不成功的试验中验证结果，调整进一步的混杂因素，并检查其他治疗领域。FDA、制造商和试验人员必须努力进行低偏倚风险的稳健临床试验。
Cochrane Database Syst Rev. 2022-2-1
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