Liu Rong, Liu Zheyu, Ghadessi Mercedeh, Vonk Richardus
Bayer Healthcare LLC, Whippany, NJ 07981, USA.
Bayer Healthcare LLC, Whippany, NJ 07981, USA.
Contemp Clin Trials. 2017 Dec;63:67-72. doi: 10.1016/j.cct.2017.06.009. Epub 2017 Jun 16.
With the rapid growth of targeted and immune-oncology therapies, novel statistical design approaches are needed to increase the flexibility and efficiency of early phase oncology trials. Basket trials enroll patients with defined biological deficiencies, but with multiple histologic tumor types (or indications), to discover in which indications the drug is active. In such designs different indications are typically analyzed independently. This, however, ignores potential biological similarities among the indications. Our research provides a statistical methodology to enhance such basket trials by assessing the homogeneity of the response rates among indications at an interim analysis, and applying a Bayesian hierarchical modeling approach in the second stage if the efficacy is deemed reasonably homogenous across indications. This increases the power of the study by allowing indications with similar response rates to borrow information from each other. Via simulations, we quantify the efficiency gain of our proposed approach relative to the conventional parallel approach. The operating characteristics of our method depend on the similarity of the response rates between the different indications. If the response rates are comparable in most or all indications after treatment with the investigational drug, a substantial increase in efficiency as compared to the conventional approach can be obtained as fewer patients are required or a higher power is attained. We also demonstrate that efficacy again decreases if the response rates vary considerably among tumor types but it is still better than the conventional approach.
随着靶向和免疫肿瘤疗法的迅速发展，需要新的统计设计方法来提高早期肿瘤试验的灵活性和效率。篮子试验招募具有特定生物学缺陷但患有多种组织学肿瘤类型（或适应症）的患者，以发现药物在哪些适应症中具有活性。在这种设计中，不同的适应症通常独立进行分析。然而，这忽略了适应症之间潜在的生物学相似性。我们的研究提供了一种统计方法，通过在中期分析时评估各适应症之间缓解率的同质性，并在第二阶段应用贝叶斯分层建模方法（如果各适应症的疗效被认为相当同质）来加强此类篮子试验。这通过允许缓解率相似的适应症相互借鉴信息，提高了研究的效能。通过模拟，我们量化了我们提出的方法相对于传统平行方法的效率提升。我们方法的操作特性取决于不同适应症之间缓解率的相似性。如果在用研究药物治疗后大多数或所有适应症的缓解率相当，那么与传统方法相比，可以实现效率的大幅提高，因为所需患者更少或效能更高。我们还证明，如果肿瘤类型之间的缓解率差异很大，疗效会再次下降，但仍优于传统方法。
