Population Pharmacokinetics/Dynamic: Statistical Issues

描述(由申请人提供)：这项提案要求提供资金继续研究，旨在开发统计模型和方法，以帮助医学肿瘤学家找到用药物治疗癌症患者的最佳方法。了解患者间异质性的多种来源是个体化治疗的关键。癌症患者的个体化治疗尤为重要，因为大多数癌症化疗的治疗窗口相对较窄。我们提出的统计模型将有助于发现患者对治疗反应的变异来源，从而允许个体化抗癌化疗。这一应用的总体假设是，了解抗癌药的药代动力学(PK)、药效学(PD)和药物遗传学(PGx)对于评估疗效和确定如何最好地在临床上使用这些药物非常重要。新的统计方法将有助于更有效地综合这些信息，以改善癌症患者的治疗结果。我们的建议有四个具体目标。(1)发展决策理论方法，促进剂量个体化。我们的方法结合了患者特定的PK信息和来自以不同剂量治疗的其他患者的PK数据，以帮助确定当前患者的最佳剂量。(2)建立连贯的统计模型，以了解PK摘要和相关SNP的依赖关系。这些方法将通过联合模型促进对药物及其代谢物和相关基因信息的PK的发现。(3)建立嵌套在重复周期内的重复数据的贝叶斯非参数模型。这些数据通常出现在临床研究中，其中感兴趣的可能是研究患者内部跨周期的相关性结构，或者了解影响结果的患者特定特征，同时考虑重复-重复测量结构。(4)构建一个同时模拟PK和PD数据的框架，将PK和PD反应视为功能反应，而不是关注几个低维的总结。我们将开发一个联合概率模型，以PK为解释变量，以纵向PD响应为结果，对函数值数据进行回归。基本的方法论主题是有效利用在研究过程中可以收集的所有数据，利用适当传播不确定性的模型对未知量进行联合推理，并试图将感兴趣的科学问题作为统计推理问题，并在适用的情况下作为决策问题。

DESCRIPTION (provided by applicant): This proposal requests funding to continue research aimed at developing statistical models and methods to help medical oncologists find the best way to treat cancer patients with pharmaceutical agents. Understanding the many sources of between-patient heterogeneity is key to individualizing therapy. Individualizing therapy for cancer patients is particularly important, because of the relatively narrow therapeutic window of most cancer chemotherapy. Our proposed statistical models will aid the discovery of sources of variation in a patient's response to therapy, allowing individualizing anticancer chemotherapy. The overall hypothesis of this application is that understanding the pharmacokinetics (PK), pharmacodynamics (PD), and pharmacogenetics (PGx) of anticancer agents is important for evaluating efficacy and determining how best to use such agents clinically. New statistical methodology will help synthesize this information more efficiently to improve treatment outcomes for cancer patients. Our proposal has four specific aims. (1) Develop a decision-theoretic approach to facilitate dose individualization. Our approach combines patient-specific PK information with PK data from other patients treated at a range of doses to help determine an optimal dose for the current patient. (2) Build coherent statistical models to learn about the dependence of PK summaries and related SNPs. The methods will foster discovery through joint models for PK of a drug, its metabolites, and related genotype information. (3) Develop Bayesian nonparametric models for repeated data nested within repeating cycles. These data often arise in clinical studies, where interest may concern studying the within-patient correlation structure across cycles or learning about patient-specific characteristics that influence outcomes while accounting for the repeated-repeated measurement structure. (4) Construct a framework to model PK and PD data simultaneously, considering PK and PD responses as functional responses, rather than focusing on a few low-dimensional summaries. We will develop a joint probability model that will implement regression for function-valued data, considering PK as the explanatory variable and the longitudinal PD response as the outcome. Underlying methodological themes are the efficient use of all data that can be collected in the course of a study, joint inference on unknown quantities with models that appropriately propagate uncertainties, and an attempt to cast the scientific questions of interest as statistical inference questions and, where applicable, as decision problems.