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Statistical modeling to support population and translational cancer research

支持人口和转化癌症研究的统计模型

基本信息

批准号：
10733838
负责人：
Roman Gulati
金额：
$ 24.52万
依托单位：
FRED HUTCHINSON CANCER CENTER
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-20 至 2028-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10733838
关键词：
Address Award Basic Science Biometry Biostatistical Methods Cancer Control Cancer Detection Cancer Diagnostics Cancer Model Cancer Research Project Cancer Surveillance Research Program Clinical Clinical Trials Collaborations Communication Computer Models Data Analyses Data Analytics Diagnostic tests Early Diagnosis Early treatment Evaluation Fostering Generations Lead Leadership Malignant neoplasm of prostate Methods Modeling Natural History Outcome Pathway interactions Patients Peer Review Phase Policies Policy Developments Population Precision therapeutics Principal Investigator Productivity Program Research Project Grants Protocols documentation Reduce health disparities Research Research Support Scientific Inquiry Screening for cancer Services Specialist Statistical Data Interpretation Statistical Models Study models Technology Uncertainty biomathematics care delivery computer program data modeling detection test diagnostic technologies experimental study health disparity improved novel programs research study screening skills sound translational cancer research treatment strategy

项目摘要

PROJECT SUMMARY/ABSTRACT Developing effective strategies for cancer control requires rigorous analysis of empirical evidence and similarly rigorous analysis to address inevitable gaps in empirical evidence. During the previous project period, the Principal Investigator provided critical statistical support for a broad spectrum of research studies focused on prostate cancer, ranging from basic science experiments through clinical trials and population modeling studies. In this renewal application, he will continue to provide biostatistical analysis services for a multi-project translational prostate cancer research program and computer modeling expertise for a cancer surveillance research program that uses biomathematical models to address evidence gaps and inform population prostate cancer policies. He will also serve as lead modeler in the Data Modeling and Analytics Core of a new multi-project research program that will investigate clinical utility of novel cancer diagnostic technologies and multi-cancer early detection tests. Successfully supporting these research programs requires broad expertise in biostatistical methods, computer programming, and data analysis to identify and implement appropriate methods, interrogate model assumptions, and bracket uncertainty in results. Outstanding scientific collaboration and communication skills are essential to explain strengths and limitations of selected approaches and to bring projects from the scientific inquiry phase through peer review so that their results can impact the delivery of care. During the previous project period, the Principal Investigator consistently demonstrated all these skills and has been instrumental in establishing his research group's leadership in cancer modeling for evidence generation and policy development. Planned studies for the renewal period include modeling of natural history and outcomes for new multi-cancer early detection tests, investigating outcomes of strategies for precision early detection and treatment, and identifying targeted screening and treatment strategies to reduce health disparities. Renewal of the Principal Investigator's Research Specialist award will facilitate his research group's continued stellar level of productivity and foster his continued dissemination of high-impact research characterized by statistical and modeling approaches that are transparent, rigorous, and reliable.

项目概要/摘要制定有效的癌症控制策略需要对经验证据进行严格分析同样严格的分析来解决经验证据中不可避免的差距。在上一个项目期间在此期间，首席研究员为广泛的研究提供了关键的统计支持专注于前列腺癌，范围从基础科学实验到临床试验和人群建模研究。在本次续签申请中，他将继续为一家公司提供生物统计分析服务。多项目转化前列腺癌研究计划和癌症的计算机建模专业知识使用生物数学模型来解决证据差距并提供信息的监测研究计划人口前列腺癌政策。他还将担任数据建模和分析领域的首席建模师新的多项目研究计划的核心，将调查新型癌症诊断的临床实用性技术和多种癌症早期检测测试。成功支持这些研究计划需要生物统计方法、计算机编程和数据分析方面的广泛专业知识来识别和实施适当的方法，询问模型假设，并确定结果的不确定性。杰出的科学协作和沟通技巧对于解释优势和优势至关重要所选方法的局限性以及将项目从科学探究阶段引入同行进行审查，以便他们的结果能够影响护理的提供。在上一个项目期间，校长调查员始终如一地展示了所有这些技能，并在建立他的研究小组在用于证据生成和政策制定的癌症建模方面的领导地位。计划更新期的研究包括自然史建模和新的多种癌症早期结果检测测试，调查精确早期检测和治疗策略的结果，以及确定有针对性的筛查和治疗策略，以减少健康差异。校长续任研究员研究专家奖将促进他的研究小组继续保持一流水平生产力并促进他继续传播以统计和研究为特征的高影响力研究透明、严格且可靠的建模方法。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A Clinical Decision Aid to Support Personalized Treatment Selection for Patients with Clinical T1 Renal Masses: Results from a Multi-institutional Competing-risks Analysis.

DOI：
10.1016/j.eururo.2021.11.002
发表时间：
2022-06
期刊：
EUROPEAN UROLOGY
影响因子：
23.4
作者：
Psutka, Sarah P.;Gulati, Roman;Jewett, Michael A. S.;Fadaak, Kamel;Finelli, Antonio;Legere, Laura;Morgan, Todd M.;Pierorazio, Phillip M.;Allaf, Mohamad E.;Herrin, Jeph;Lohse, Christine M.;Thompson, R. Houston;Boorjian, Stephen A.;Atwell, Thomas D.;Schmit, Grant D.;Costello, Brian A.;Shah, Nilay D.;Leibovich, Bradley C.
通讯作者：
Leibovich, Bradley C.

A Quantitative Framework to Study Potential Benefits and Harms of Multi-Cancer Early Detection Testing.