Advanced statistical modeling and analytics for prostate cancer interventions

前列腺癌干预的高级统计建模和分析

• 批准号: 10603102
• 负责人: Roman Gulati
• 金额: $ 13.53万
• 依托单位: FRED HUTCHINSON CANCER CENTER
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-20 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10603102
• 关键词: Advanced; statistical; modeling; analytics; prostate

PROJECT SUMMARY Developing sound strategies for prostate cancer control requires integrating information from a vast collection of clinical trials and observational studies and developing statistical and computer models to fill in inevitable evidence gaps. Management of prostate cancer is particularly controversial because of the absence of a consensus about optimal screening and treatment approaches. The PI of this application is a member of a team that develops statistical methods and computer models to advance sound prostate cancer policies. This work can only be done by a skilled specialist in cancer surveillance and statistical modeling. It demands expertise in complex statistical computing, which must be meticulously executed and validated, internally and externally. It requires outstanding writing skills to bring projects from the inquiry phase through the publication process, and it requires a proven ability to communicate and collaborate across multidisciplinary teams. The PI of this application has used these skills and others to establish his research group as a leader in prostate cancer modeling and policy. In this application he proposes to use these skills in support of new modeling analyses including (1) targeted screening in high-risk patient subgroups, (2) frameworks for decision making around novel biomarkers for screening and treatment, and (3) advancing methods to estimate overdiagnosis— the detection by screening of cancers that would never be diagnosed otherwise. Additionally, this application will support (1) developing online calculators and decision aids to assist clinicians and policymakers in selecting appropriate recommendations and (2) providing high-quality biostatistical service in a multi-project translational prostate cancer research program. The overarching aim is to optimize interventions to reduce morbidity and mortality from this most common cancer in men.

项目总结 开发合理的前列腺癌控制策略需要整合来自大量收集的信息 临床试验和观察性研究以及开发统计和计算机模型来填补不可避免的 证据空白。前列腺癌的治疗尤其有争议，因为没有一种 就最佳筛查和治疗方法达成共识。此应用程序的PI是 开发统计方法和计算机模型以推进健全的前列腺癌政策的团队。这 这项工作只能由癌症监测和统计建模方面的熟练专家来完成。它要求 在复杂的统计计算方面的专业知识，必须仔细执行和验证，内部和 从外部来说。它需要出色的写作技巧，才能将调查阶段的项目带到出版物上 它需要经过验证的跨多学科团队沟通和协作的能力。《少年派》 利用这些技能和其他技能建立了他的研究小组，成为前列腺领域的领先者 癌症模型和政策。在本应用程序中，他建议使用这些技能来支持新的建模 分析包括(1)对高危患者亚组进行有针对性的筛查，(2)决策框架 围绕筛查和治疗的新生物标志物，以及(3)估计过度诊断的先进方法-- 通过筛查永远不会被诊断出的癌症来进行检测。此外，此应用程序 将支持(1)开发在线计算器和决策辅助工具，以帮助临床医生和决策者 选择合适的建议和(2)在多个项目中提供高质量的生物统计服务 转化型前列腺癌研究计划。总体目标是优化干预措施，以减少 这种男性最常见的癌症的发病率和死亡率。