Department of Biostatistics, University of California, Los Angeles, California, USA.
Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA.
J Biopharm Stat. 2022 Jul 4;32(4):613-626. doi: 10.1080/10543406.2022.2089160. Epub 2022 Jun 23.
It is crucial in clinical trials to investigate treatment effect consistency across subgroups defined by patient baseline characteristics. However, there may be treatment effect variability across subgroups due to small subgroup sample size. Various Bayesian models have been proposed to incorporate this variability when borrowing information across subgroups. These models rely on the underlying assumption that patients with similar characteristics will have similar outcomes to the same treatment. Patient populations within each subgroup must subjectively be deemed similar enough Pocock (1976) to borrow response information across subgroups. We propose utilizing the machine learning method of Bayesian Additive Regression Trees (BART) to provide a method for subgroup borrowing that does not rely on an underlying assumption of homogeneity between subgroups. BART is a data-driven approach that utilizes patient-level observations. The amount of borrowing between subgroups automatically adjusts as BART learns the covariate-response relationships. Modeling patient-level data rather than treating the subgroup as a single unit minimizes assumptions regarding homogeneity across subgroups. We illustrate the use of BART in this context by comparing performance from existing subgroup borrowing methods in a simulation study and a case study in non-small cell lung cancer. The application of BART in the context of subgroup analyses alleviates the need to subjectively choose how much information to borrow based on subgroup similarity. Having the amount of borrowing be analytically determined and controlled for based on the similarity of individual patient-level characteristics allows for more objective decision making in the drug development process with many other applications including basket trials.
在临床试验中，调查根据患者基线特征定义的亚组之间的治疗效果一致性至关重要。然而，由于亚组样本量小，可能会存在治疗效果的变异性。已经提出了各种贝叶斯模型来在跨亚组借用信息时纳入这种可变性。这些模型依赖于一个基本假设，即具有相似特征的患者将对相同的治疗有相似的结果。在每个亚组内，患者群体必须主观上被认为足够相似（Pocock，1976），以便跨亚组借用反应信息。我们建议利用机器学习方法贝叶斯加法回归树（BART）来提供一种不依赖亚组之间同质性的假设的亚组借用方法。BART 是一种数据驱动的方法，利用患者水平的观察结果。随着 BART 学习协变量-反应关系，亚组之间的借用量会自动调整。对患者水平数据进行建模而不是将亚组视为单个单元，可最大限度地减少亚组之间同质性的假设。我们通过在模拟研究和非小细胞肺癌案例研究中比较现有亚组借用方法的性能，说明了 BART 在这种情况下的应用。在亚组分析的背景下应用 BART，可以减轻基于亚组相似性主观选择借用多少信息的需要。根据个体患者水平特征的相似性，通过分析确定借用量并进行控制，可在药物开发过程中进行更客观的决策，并具有许多其他应用，包括篮子试验。
