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Bayesian non-parametric survival regression for optimizing precision dosing of intravenous busulfan in allogeneic stem cell transplantation.
The Journal of the Royal Statistical Society: Series C (Applied Statistics) ( IF 1.0 ) Pub Date : 2018-12-16 , DOI: 10.1111/rssc.12331
Yanxun Xu 1 , Peter F Thall 2 , William Hua 1 , Borje S Andersson 3
Affiliation  

Allogeneic stem cell transplantation (allo-SCT) is now part of standard of care for acute leukemia (AL). To reduce toxicity of the pre-transplant conditioning regimen, intravenous busulfan is usually used as a preparative regimen for AL patients undergoing allo-SCT. Systemic busulfan exposure, characterized by the area under the plasma concentration versus time curve (AUC), is strongly associated with clinical outcome. An AUC that is too high is associated with severe toxicities, while an AUC that is too low carries increased risks of disease recurrence and failure to engraft. Consequently, an optimal AUC interval needs to be determined for therapeutic use. To address the possibility that busulfan pharmacokinetics and pharmacodynamics vary significantly with patient characteristics, we propose a tailored approach to determine optimal covariate-specific AUC intervals. To estimate these personalized AUC intervals, we apply a flexible Bayesian nonparametric regression model based on a dependent Dirichlet process and Gaussian process, DDP-GP. Our analyses of a dataset of 151 patients identified optimal therapeutic intervals for AUC that varied substantively with age and whether the patient was in complete remission or had active disease at transplant. Extensive simulations to evaluate the DDP-GP model in similar settings showed that its performance compares favorably to alternative methods. We provide an R package, DDPGPSurv, that implements the DDP-GP model for a broad range of survival regression analyses.

中文翻译:


贝叶斯非参数生存回归优化同种异体干细胞移植中静脉注射白消安的精确剂量。



同种异体干细胞移植(allo-SCT)现已成为急性白血病(AL)标准治疗的一部分。为了减少移植前预处理方案的毒性,静脉注射白消安通常用作接受异基因 SCT 的 AL 患者的准备方案。全身白消安暴露以血浆浓度与时间曲线下面积 (AUC) 为特征,与临床结果密切相关。 AUC 过高与严重毒性相关,而 AUC 过低则会增加疾病复发和移植失败的风险。因此,需要确定治疗用途的最佳 AUC 间隔。为了解决白消安药代动力学和药效学随患者特征显着变化的可能性,我们提出了一种定制方法来确定最佳协变量特异性 AUC 区间。为了估计这些个性化的 AUC 区间,我们应用了基于依赖狄利克雷过程和高斯过程 DDP-GP 的灵活贝叶斯非参数回归模型。我们对 151 名患者的数据集进行分析,确定了 AUC 的最佳治疗间隔,该治疗间隔随年龄以及患者是否处于完全缓解状态或在移植时患有活动性疾病而发生显着变化。在类似设置中评估 DDP-GP 模型的广泛模拟表明,其性能优于其他方法。我们提供了一个 R 包 DDPGPSurv,它实现了 DDP-GP 模型以进行广泛的生存回归分析。
更新日期:2019-11-01
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