Department of Population Health, New York University Grossman School of Medicine, 180 Madison Avenue, New York, 10016, New York, USA.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, 02115, MA, USA.
BMC Med Res Methodol. 2024 Sep 27;24(1):218. doi: 10.1186/s12874-024-02333-z.
Precision medicine has led to the development of targeted treatment strategies tailored to individual patients based on their characteristics and disease manifestations. Although precision medicine often focuses on a single health outcome for individualized treatment decision rules (ITRs), relying only on a single outcome rather than all available outcomes information leads to suboptimal data usage when developing optimal ITRs.
To address this limitation, we propose a Bayesian multivariate hierarchical model that leverages the wealth of correlated health outcomes collected in clinical trials. The approach jointly models mixed types of correlated outcomes, facilitating the "borrowing of information" across the multivariate outcomes, and results in a more accurate estimation of heterogeneous treatment effects compared to using single regression models for each outcome. We develop a treatment benefit index, which quantifies the relative benefit of the experimental treatment over the control treatment, based on the proposed multivariate outcome model.
We demonstrate the strengths of the proposed approach through extensive simulations and an application to an international Coronavirus Disease 2019 (COVID-19) treatment trial. Simulation results indicate that the proposed method reduces the occurrence of erroneous treatment decisions compared to a single regression model for a single health outcome. Additionally, the sensitivity analyses demonstrate the robustness of the model across various study scenarios. Application of the method to the COVID-19 trial exhibits improvements in estimating the individual-level treatment efficacy (indicated by narrower credible intervals for odds ratios) and optimal ITRs.
The study jointly models mixed types of outcomes in the context of developing ITRs. By considering multiple health outcomes, the proposed approach can advance the development of more effective and reliable personalized treatment.
精准医学导致了针对患者个体特征和疾病表现的靶向治疗策略的发展。虽然精准医学通常侧重于针对个体化治疗决策规则 (ITR) 的单一健康结果，但仅依赖单一结果而不是所有可用结果信息会导致在开发最佳 ITR 时数据使用不理想。
为了解决这一限制，我们提出了一种贝叶斯多变量分层模型，利用临床试验中收集的丰富相关健康结果信息。该方法联合建模了混合类型的相关结果，促进了多变量结果之间的“信息借用”，并与针对每个结果使用单个回归模型相比，更准确地估计了异质治疗效果。我们根据提出的多变量结果模型开发了一个治疗效益指数，该指数量化了实验治疗相对于对照治疗的相对效益。
我们通过广泛的模拟和对一项国际冠状病毒病 2019 (COVID-19) 治疗试验的应用，展示了所提出方法的优势。模拟结果表明，与针对单一健康结果的单个回归模型相比，该方法减少了错误治疗决策的发生。此外，敏感性分析表明，该模型在各种研究场景下具有稳健性。该方法在 COVID-19 试验中的应用表明，在估计个体治疗效果（表现为优势比的置信区间更窄）和最佳 ITR 方面有所改进。
本研究在开发 ITR 背景下联合建模了混合类型的结果。通过考虑多个健康结果，所提出的方法可以推进更有效和可靠的个性化治疗的发展。
