Project 1: Innovative Biomarker-Integrated Clinical Trial Design and Analysis

项目1：创新生物标记物--综合临床试验设计与分析 项目总结 随着生物医学技术的进步，生物标志物在疾病进展中发挥着越来越重要的作用。 癌症患者的诊断和治疗选择。结合生物标记物的创新临床试验设计 信息可以提高研究效率，减少患者，降低成本，从而加速癌症 药物开发。这项研究项目的广泛、长期目标是发展创新的统计学 解决生物标记物综合临床试验设计和分析中问题的方法学。确实有 这个项目的四个具体目标。第一个目标是开发设计和分析方法，以确定最优 基于生物标记物的亚组或具有事件发生时间终点的临床试验的最佳生物标记物切入点。 将为非随机化试验和随机化试验开发统计方法。第二个目标是-- 盖茨的个性化治疗策略适应于预随机化的纵向生物标记物。统计 基于选择模型的方法将被用于联合建模纵向生物标记物和生存结果。 用于检验纵向生物标记物与治疗之间相互作用的样本量和能量计算 将会被开发出来。第三个目标是开发生物标记物浓缩临床的设计和分析方法。 使用代理依赖抽样和标记依赖抽样的试验。当标志物阳性的流行率为 传统设计要求样本量大，这限制了许多研究的可行性。 将研究一类新的基于生物标记物的高性价比设计及其实施相关问题 在设计中，将研究效率增益的条件和新估计的渐近性质。 用于量化利用生物标记物指导治疗所产生的益处的新的统计方法将 也被调查。第四个目标是提出发现个性化预测的两阶段临床试验。 生物标志物。将探索两阶段设计，并给出该两阶段设计的样本量和功率公式 将被开发，特别是对于新颖的第二阶段，其中来自第一阶段的辅助信息 注册成立。对于所有的目标，我们将(1)研究所提出的方法的理论属性 基于现代经验过程理论和其他先进的统计理论；(2)绩效考核 透过广泛的模拟研究，将建议的方法应用于实际环境；及。(三)制订方便使用的方法；及。 开发的方法以及样本量和功率计算工具的软件，并加以传播 向公众免费开放。

Project 1: Innovative Biomarker-Integrated Clinical Trial Design and Analysis PROJECT SUMMARY With advances in biomedical technology, biomarkers are playing an increasingly important role in disease prog- nosis and treatment selection for cancer patients. Innovative clinical trial designs that incorporate biomarker information can improve study efficiency, require fewer patients, and reduce costs, thereby accelerating cancer drug development. The broad, long-term objective of this research project is to develop innovative statistical methodology to address issues in the design and analysis of biomarker-integrated clinical trials. There are four specific aims in this project. The first aim develops design and analysis methods to identify the optimal biomarker-based subgroups or the optimal biomarker cut-point for clinical trials with time-to-event endpoints. Statistical methods will be developed for both non-randomized and randomized trials. The second aim investi- gates a personalized treatment strategy that is adaptive to pre-randomization longitudinal biomarkers. Statistical methods based on selection models will be used to model longitudinal biomarkers and survival outcomes jointly. Sample size and power calculations for testing the interactions between longitudinal biomarkers and treatments will be developed. The third aim is to develop design and analysis methods for biomarker-enrichment clinical trials using surrogate-dependent and marker-dependent sampling. When the prevalence of marker positive pa- tients is low, the traditional design requires a large sample size, which can limit the feasibility of many studies. A new class of biomarker-based cost-effective designs will be studied and issues related to the implementation of the design, conditions for efficiency gain, and asymptotic properties of the new estimates will be investigated. Novel statistical measures for quantifying the benefit resulting from utilizing biomarkers to direct treatments will also be investigated. The fourth aim proposes two-phase clinical trials for discovering personalized predictive biomarkers. A two-phase design will be explored and sample size and power formulae for this two-phase design will be developed, especially for the novel second phase where auxiliary information from the first phase will be incorporated. For all of the aims, we will (1) investigate the theoretical properties of the proposed methodology based on modern empirical process theory and other advanced statistical theory; (2) examine the performance of the proposed methods in practical settings through extensive simulation studies; and (3) develop user-friendly software for the developed methods and for the sample size and power calculation tools and disseminate them freely to the general public.