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：针对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学剂量（OBD）的合并方法忽略了不同适应症间的剂量-反应或剂量-毒性曲线可能存在差异，从而导致OBD各不相同。反之，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，我们提出了一种随机两阶段篮式试验设计，用于优化多适应症剂量（ROMI）。在第一阶段，针对每种适应症，评估高剂量（可能为先前获得的MTD）的反应和毒性，并采用一项规则：若高剂量对某适应症不安全或无效，则停止纳入该适应症的患者。未终止的适应症进入第二阶段，患者被随机分配至高剂量和指定的较低剂量之间。我们采用一种潜在聚类贝叶斯分层模型，在考虑不同适应症OBD潜在异质性的同时，实现适应症间的信息借用。特定适应症的效用函数被用于量化反应与毒性之间的权衡。在第二阶段结束时，对于至少存在一种可接受剂量的适应症，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ROMI的两种版本：一种仅使用第二阶段数据进行剂量优化，另一种则使用两个阶段的数据进行剂量优化。模拟结果表明，与忽略适应症差异或独立优化各适应症剂量的设计方案相比，两种ROMI版本均展现出更优的操作特性。