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A Simulation Model-based Framework to Support Oncology Guidelines and Practice

支持肿瘤学指南和实践的基于仿真模型的框架

基本信息

批准号：
9977402
负责人：
Jinani Jayasekera
金额：
$ 17.54万
依托单位：
GEORGETOWN UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-04-01 至 2022-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9977402
关键词：
Address Affect Age American Society of Clinical Oncology Award Big Data Cancer Diagnostics Cancer Intervention and Surveillance Modeling Network Cancer Model Caring Characteristics Clinical Clinical Practice Guideline Complex Coupled Data Data Discovery Data Sources Decision Making Development Diagnosis Discipline Disease ERBB2 gene Endocrine Equilibrium Event Foundations Funding Future Gene Expression Profile Genomics Goals Grant Guidelines Health Policy Individual Institute of Medicine (U.S.)Institutes Intervention Knowledge Left Malignant Neoplasms Mentors Methods Mission Modeling Molecular Oncology Outcome Patients Policies Population Process Publications Race Recurrence Research Research Personnel Research Training Resources Scientist Solid Source Suggestion Test Result Testing Toxic effect Training Translational Research Translations Treatment outcome Uncertainty Validation Woman Women&apos s Group Work aged base cancer care cancer therapy career chemotherapy clinical care clinical decision-making clinical development clinical practice comorbidity data sharing design diverse data experience experimental study follow-up hormone receptor-positive hormone therapy individual patient innovation interest knowledge base malignant breast neoplasm mathematical model models and simulation mortality multidisciplinary personalized cancer care response secondary analysis shared decision making skills tool treatment choice treatment effect trend trial design tumor tumor progression virtual laboratory web interface

项目摘要

Abstract Personalized cancer care is complex in this unprecedented era of discovery and big data. The large and evolving knowledge base requires clinicians and policymakers to synthesize diverse data to design new trials, inform clinical guidelines, practice, and policy. The Institute of Medicine and others have recommended the use of simulation modeling in these situations to synthesize evidence and support clinical care. Simulation modeling involves the use of mathematical models to combine various sources of evidence to assess how interventions could alter the progression of a disease and affect outcomes. My overarching goal is to use this K99/R00 award to gain the necessary skills and experience to become an independent researcher and leader in the use of simulation modeling in oncology. This revised application includes training in modeling methods needed to fill gaps in my knowledge and use that training to build a new simulation model to address gene expression profile (GEP)-guided care for the nearly 180,000 US women annually diagnosed with early-stage breast cancer. The training aims are: Aim K1. Apply training in data discovery and synthesis to develop input parameters to model the effects of chemo- endocrine (vs. endocrine) therapy on cancer outcomes such as recurrence, mortality, and chemotherapy- related toxicity based on patient (e.g., age, race, comorbidity) and tumor (e.g., tumor size, grade) characteristics, and GEP test results; Aim K2. Apply training in simulation modeling to build a model combining input parameters from aim K1 to project cancer outcomes; and Aim K3. Apply training in uncertainty quantification to estimate the variability associated with modeled outcomes to place results in context for clinicians and guideline developers. The research aims are: Aim R1. Perform model validation to demonstrate the model's ability to reproduce predictions using an independent data source that was not used in aim K1; make necessary revisions to the model; Aim R2. Use the validated model to provide a summary of the balance of benefits and harms of chemotherapy in exemplar groups of women to support the development of clinical guidelines; and Aim R3. Create an interactive web-interface to provide model results on the effects of chemo-endocrine (vs. endocrine) therapy on cancer outcomes based on individual characteristics. I am uniquely qualified for this award given my strong track record of pilot funding, 21 high impact publications, a solid quantitative research foundation, commitment to a research career, and preliminary modeling research with my multidisciplinary mentoring team. The exceptional institutional resources coupled with the Cancer Intervention and Surveillance Modeling Network (CISNET) provide the ideal setting for this application. The integrated research and training will leave me poised to become an independent researcher using simulation modeling to assist in the translation of rapidly evolving knowledge into oncology care.

摘要