POPULATION PHARMACOKINETICS/DYNAMICS: STATISTICAL ISSUES

癌症的临床研究正变得越来越复杂。我们对肿瘤细胞生长的分子特征的理解正在为抗癌药物的开发带来新的想法，这给临床研究的设计和分析带来了挑战，以评估其活性。仍然需要更好地了解常用抗癌药物以及这些新药物的药代动力学和药效学中的可变性决定因素。大多数(如果不是全部)抗癌药物最初是在样本量相对较小的早期临床研究中进行最密切检查的，这一事实要求采用有效和连贯地结合从单独研究收集的信息的方法。针对这些和类似的问题，本申请书描述了分析和设计复杂数据研究的统计方法的持续研究和开发。第一个具体目标涉及具有混合先验的相关数据的分层建模，并将方法扩展到在多个周期治疗和/或在多个终点收集的数据(例如，同时给药的两种或两种以上药物的药代动力学)。第二个具体目标是组合来自研究的信息，这些研究产生相关数据(例如，来自同一患者的多次活组织检查的纵向轮廓或化验结果)，并且可能有或多或少的共同点。该应用程序描述了统计工具的发展，以便对具有这种相关结果数据的研究进行全面的贝叶斯荟萃分析。重要的是，统计模型还允许混合模型中的公共成分和研究特定成分，并以数据依赖的方式推断模型允许跨研究借用强度的程度。第三个具体目标提出了在建立具有混合先验的复杂生物过程的背景下确定样本量、采样时间和/或每个患者(或肿瘤)的样本数的最优设计规则。这些设计将允许在随后的周期中顺序更新未来主题或当前主题的设计点(例如，采样时间)。拟议的工作将探索这些设计相对于其他可能的设计策略的特性。

Clinical research in cancer is becoming ever more complex. Our understanding of the molecular characteristics of tumor cell growth is spawning new ideas for anticancer drug development that present challenges for the design and analysis of clinical studies to evaluate their activity. There still exists a need for better understanding of the determinants of variability in the pharmacokinetics and pharmacodynamics of commonly used anticancer drugs, as well as of these newer agents. The fact that most, if not all, anticancer agents are initially examined most closely in early phase clinical studies having relatively small sample sizes begs for methods that will combine information gathered from separate studies efficiently and coherently. Addressing these and similar issues, this application describes continuing research and development of statistical methods for the analysis and design of studies of complex data. The first specific aim concerns hierarchical modeling of correlated data with mixture priors and expands the methods to data from patients treated over multiple cycles and/or collected for multiple end points (e.g., pharmacokinetics of two or more drugs given simultaneously). The second specific aim addresses combining information from studies that generate correlated data (e.g., longitudinal profiles or assay results from multiple biopsies from the same patient) and that may have more or less in common. The application describes the development of statistical tools to carry out full Bayesian meta-analysis across studies with such correlated outcome data. Importantly, the statistical model also allows for common and study-specific components in the mixture model and for inferring in a data-dependent way the degree to which the model allows borrowing strength across studies. The third specific aim proposes optimal design rules for determining sample size, sampling times and/or numbers of samples per patient (or tumor) in the context of modeling complex biologic processes with mixture priors. The designs will allow for sequential updating of design points (e.g., sampling times) for future subjects or current subjects in subsequent cycles. The proposed work will explore the properties of these designs relative to other possible design strategies.