Statistical Design & Analysis of Clinical Studies using Personalised Healthcare under Biomarker Uncertainty

统计设计

• 批准号: 2106026
• 金额: --
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2106026
• 关键词: Statistical; Design; Analysis; Clinical; Studies

Programme overview:This MRC-funded doctoral training partnership (DTP) brings together cutting-edge molecular and analytical sciences with innovative computational approaches in data analysis to enable students to undertake important applied biomedical research in partnership with industry. This is a 4-year programme whose first year involves a series of taught modules and two laboratory-based research projects that lead to an MSc in Interdisciplinary Biomedical Research. The first two terms consist of a selection of taught modules that allow students to gain a solid grounding in multidisciplinary science. Students also attend a series of masterclasses led by academic and industry experts in areas of molecular, cellular and tissue dynamics, microbiology and infection, applied biomedical technologies and artificial intelligence and data science. During the third and summer terms students conduct two eleven-week research projects in labs of their choice. Project:Statistical design and analysis of clinical studies using personalised healthcare under biomarker uncertaintyIn collaboration with Roche (Industry partner), the PhD student will address an important issue on the role played by continuous biomarkers in the identification of patients most likely to benefit from a particular treatment within the setting of Personalised HealthCare. In order to identify cut-offs that define the targeted subgroup of patients, novel statistical and machine learning methods will be used on large datasets from clinical trials. The research problem shall form a crucial part of the biomarker-driven drug development programme from a pharmaceutical industry perspective.

项目概述：这一由 MRC 资助的博士培训合作伙伴关系 (DTP) 将尖端的分子和分析科学与数据分析中的创新计算方法结合在一起，使学生能够与行业合作开展重要的应用生物医学研究。这是一个为期 4 年的课程，第一年涉及一系列教学模块和两个基于实验室的研究项目，最终获得跨学科生物医学研究硕士学位。前两个学期包括精选的教学模块，使学生能够在多学科科学方面打下坚实的基础。学生还参加由分子、细胞和组织动力学、微生物学和感染、应用生物医学技术以及人工智能和数据科学领域的学术和行业专家主持的一系列大师班。在第三学期和夏季学期，学生在自己选择的实验室进行两个为期十一周的研究项目。项目：在生物标志物不确定性下使用个性化医疗保健进行临床研究的统计设计和分析与罗氏（行业合作伙伴）合作，博士生将解决连续生物标志物在识别最有可能从特定患者中受益的患者中所发挥的作用的重要问题个性化医疗保健环境下的治疗。为了确定定义目标患者亚组的临界点，将在临床试验的大型数据集上使用新颖的统计和机器学习方法。从制药行业的角度来看，研究问题应成为生物标志物驱动的药物开发计划的重要组成部分。