Title: fkbma: An R Package for Detecting Tailoring Variables with Free-Knot B-Splines and Bayesian Model Averaging Show Chinese title

Title: fkbma：一个用于使用自由结点B样条和贝叶斯模型平均检测定制变量的R包

Abstract: 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. Show Chinese abstract

Abstract: 精准医学旨在通过识别最可能从特定干预中受益的患者亚组来优化治疗。 为了支持这一目标，我们引入了fkbma，这是一个R包，实现了贝叶斯模型平均方法，结合自由节点B样条用于识别定制变量。 该包采用可逆跳跃马尔可夫链蒙特卡罗算法，在考虑变量选择和非线性关系不确定性的同时，灵活地建模治疗效果的异质性。 fkbma提供了一个全面的框架，用于检测预测生物标志物，整合贝叶斯自适应丰富策略，并在临床试验和观察性研究中实现稳健的亚组识别。 本文详细介绍了fkbma背后的统计方法，概述了其计算实现，并通过模拟和实际例子展示了其应用。 该包的灵活性使其成为精准医学研究的有用工具，为治疗个性化提供了一种有原则的方法。