Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania, USA.
Res Synth Methods. 2022 Nov;13(6):807-820. doi: 10.1002/jrsm.1597. Epub 2022 Aug 26.
Meta-analysis allows researchers to combine evidence from multiple studies, making it a powerful tool for synthesizing information on the safety profiles of new medical interventions. There is a critical need to identify subgroups at high risk of experiencing treatment-related toxicities. However, this remains quite challenging from a statistical perspective as there are a variety of clinical risk factors that may be relevant for different types of adverse events, and adverse events of interest may be rare or incompletely reported. We frame this challenge as a variable selection problem and propose a Bayesian hierarchical model which incorporates a horseshoe prior on the interaction terms to identify high-risk groups. Our proposed model is motivated by a meta-analysis of adverse events in cancer immunotherapy, and our results uncover key factors driving the risk of specific types of treatment-related adverse events.
元分析使研究人员能够合并来自多个研究的证据，从而成为综合新医疗干预措施安全性特征信息的有力工具。从统计学角度来看，确定有较高风险经历治疗相关毒性的亚组至关重要。然而，这仍然具有很大的挑战性，因为可能有各种临床危险因素与不同类型的不良事件相关，并且感兴趣的不良事件可能很少或未完全报告。我们将这一挑战框定为变量选择问题，并提出了一种贝叶斯层次模型，该模型在交互项上采用了马氏先验，以识别高风险组。我们提出的模型是基于癌症免疫治疗不良事件的元分析，我们的结果揭示了驱动特定类型治疗相关不良事件风险的关键因素。
