Methods for Pharmacogenomics and Individualized Therapy Trails

这项研究的广泛和长期目标是开发新的和高影响力的统计和 用于发现与个体间疗效差异相关的遗传变异的计算工具 癌症药物的安全性和毒性，并根据每个患者的遗传基因来优化药物治疗 宪法。具体目标包括：(1)建立稳健和有效的统计方法 评估SNP基因型和单倍型对不同表型药物反应的影响(例如， 疗效和毒性的二元和连续测量、右审查生存时间、间隔审查时间 TP疾病进展、信息审查的PSA水平和不良事件)；(2) 基于高维、高维的药物反应预测的统计和数据挖掘技术 相关的基因组数据和复杂的表型；(3)统计程序调查 用复杂和高度多变量的遗传数据提供低偏差的效应大小估计，用于后续研究 和验证性研究；(4)探索一种新的机器学习形式来识别候选人 临床前研究和临床试验中的个体化治疗。所有这些目标都是由 研究人员的应用研究经验和解决最及时和最重要的问题 药物基因组学和个体化治疗。建议的解决方案建立在健全的统计和 数据挖掘原理。新方法的理论性质将通过 现代经验过程理论和其他高级数学论点。高效稳定 将设计数值算法来实施新方法。将进行广泛的模拟研究 进行的目的是评估新的推论和数值程序在 真实设置。将向大量癌症研究提供申请，其中大部分都是随身携带的 杜克大学和北卡罗来纳大学教堂山分校。实用、人性化 软件将被开发并免费向公众传播。我们的研究将改变 设计和分析药物基因组学研究和个体化治疗试验，这将导致 癌症和其他疾病患者的最佳治疗方案。

The broad, long-term objectives of this research are the development of novel and high-impact statistical and computational tools for discovering genetic variants associated with inter-individual differences in the efficacy and toxicity of cancer medications and for optimizing drug therapy on the basis of each patient's genetic constitution. The specific aims include: (1) construction of robust and efficient statistical methods for assessing the effects of SNP genotypes and haplotypes on drug response with a variety of phenotypes (e.g., binary and continuous measures of efficacy and toxicity, right-censored survival time, interval-censored time tp disease progression, and informatively censored PSA levels and adverse events); (2) development of statistical and data-mining techniques for predicting drug response based on high-dimensional, highly correlated genomic data and complex phenotypes; (3) investigation of statistical procedures for providing low-bias estimation of effect sizes with complex and highly multivariate genetic data for follow-up and confirmation studies; (4) exploration of a new form of machine learning for identifying candidate individualized therapies in both pre-clinical studies and clinical trials. All these aims have been motivated by the investigators' applied research experiences and address the most timely and important issues in pharmacogenomics and individualized therapy. The proposed solutions are built on sound statistical and data-mining principles. The theoretical properties of the new methods will be established rigorously via modern empirical process theory and other advanced mathematical arguments. Efficient and stable numerical algorithms will be devised to implement the new methods. Extensive simulation studies will be conducted to evaluate the operating characteristics of the new inferential and numerical procedures in realistic settings. Applications will be provided to a large number of cancer studies, most of which are carried out at Duke University and the University of North Carolina at Chapel Hill. Practical and user-friendly software will be developed and disseminated freely to the general public. Our research will change the ways pharmacogenomic studies and individualized therapy trials are designed and analyzed, which will lead to optimal treatments for patients in cancer and other diseases.