Precision medicine aims to optimize treatment by identifying patient subgroups most likely to benefit from specific interventions. To support this goal, we introduce fkbma, an R package that implements a Bayesian model averaging approach with free-knot B-splines for identifying tailoring variables. The package employs a reversible jump Markov chain Monte Carlo algorithm to flexibly model treatment effect heterogeneity while accounting for uncertainty in both variable selection and non-linear relationships. fkbma provides a comprehensive framework for detecting predictive biomarkers, integrating Bayesian adaptive enrichment strategies, and enabling robust subgroup identification in clinical trials and observational studies. This paper details the statistical methodology underlying fkbma, outlines its computational implementation, and demonstrates its application through simulations and real-world examples. The package's flexibility makes it a valuable tool for precision medicine research, offering a principled approach to treatment personalization.

翻译：精准医学旨在通过识别最可能从特定干预中获益的患者亚组来优化治疗方案。为实现这一目标，我们推出了fkbma——一个基于自由节点B样条的贝叶斯模型平均方法R包，用于识别定制变量。该包采用可逆跳转马尔可夫链蒙特卡洛算法，在综合考虑变量选择和非线性关系不确定性的同时，灵活建模治疗效果异质性。fkbma为检测预测性生物标志物、整合贝叶斯自适应富集策略以及在临床试验与观察性研究中实现稳健的亚组识别提供了完整框架。本文详细阐述了fkbma的统计方法论基础，概述其计算实现流程，并通过模拟实验与真实案例展示其应用场景。该包的灵活性使其成为精准医学研究的重要工具，为治疗个性化提供了理论严谨的方法路径。