Optimizing doses for multiple indications is challenging. The pooled approach of finding a single optimal biological dose (OBD) for all indications ignores that dose-response or dose-toxicity curves may differ between indications, resulting in varying OBDs. Conversely, indication-specific dose optimization often requires a large sample size. To address this challenge, we propose a Randomized two-stage basket trial design that Optimizes doses in Multiple Indications (ROMI). In stage 1, for each indication, response and toxicity are evaluated for a high dose, which may be a previously obtained MTD, with a rule that stops accrual to indications where the high dose is unsafe or ineffective. Indications not terminated proceed to stage 2, where patients are randomized between the high dose and a specified lower dose. A latent-cluster Bayesian hierarchical model is employed to borrow information between indications, while considering the potential heterogeneity of OBD across indications. Indication-specific utilities are used to quantify response-toxicity trade-offs. At the end of stage 2, for each indication with at least one acceptable dose, the dose with highest posterior mean utility is selected as optimal. Two versions of ROMI are presented, one using only stage 2 data for dose optimization and the other optimizing doses using data from both stages. Simulations show that both versions have desirable operating characteristics compared to designs that either ignore indications or optimize dose independently for each indication.

翻译：为多种适应症优化剂量具有挑战性。为所有适应症寻找单一最佳生物剂量（OBD）的合并方法，忽略了剂量-反应或剂量-毒性曲线在不同适应症间可能存在差异，从而导致OBD各不相同。相反，针对特定适应症的剂量优化通常需要较大的样本量。为应对这一挑战，我们提出了一种用于优化多适应症剂量的随机两阶段篮式试验设计（ROMI）。在第一阶段，针对每种适应症，评估一个高剂量（可能是先前获得的MTD）的反应和毒性，并采用一项规则，停止招募那些高剂量不安全或无效的适应症。未终止的适应症进入第二阶段，患者在高剂量和指定的较低剂量之间随机分配。我们采用了一种潜在聚类贝叶斯分层模型，在适应症间借用信息，同时考虑OBD在不同适应症间潜在的异质性。使用适应症特定的效用函数来量化反应与毒性之间的权衡。在第二阶段结束时，对于至少有一种可接受剂量的每种适应症，选择具有最高后验平均效用的剂量作为最优剂量。本文提出了两个版本的ROMI：一个仅使用第二阶段数据进行剂量优化，另一个则使用两个阶段的数据进行剂量优化。模拟结果表明，与那些忽略适应症差异或独立为每种适应症优化剂量的设计相比，两个版本均具有理想的运行特性。