Modeling Multi-Source Data in Hodgkin Lymphoma

霍奇金淋巴瘤的多源数据建模

• 批准号: 10579326
• 负责人: Andrew M Evens
• 金额: $ 80.82万
• 依托单位: TUFTS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10579326
• 关键词: Acute; Adult; Adult Hodgkin' s Lymphoma; Advocate; Aftercare; Age; Algorithms; Australia; Biological; Biology; British Columbia; Calibration; Cancer Patient; Cardiovascular Diseases; Cardiovascular system; Caring; Cessation of life; Characteristics; Chemotherapy and/or radiation; Clinical; Clinical Treatment; Clinical Trials; Clinical Trials Cooperative Group; Collaborations; Consensus; Cox Proportional Hazards Models; Data; Data Analytics; Data Science; Databases; Decision Making; Decision Modeling; Development; Diagnosis; Disease; Disease Outcome; Disease Progression; Dose; Epidemiologist; Europe; Excess Mortality; Future; German population; Goals; Heart; Hodgkin Disease; Hospitals; Image; Image Enhancement; Immunooncology; Individual; Insurance Claim Review; International; Iowa; Knowledge; Late Effects; Life; Life Cycle Stages; Life Expectancy; Link; Literature; Long-Term Survivors; Malignant Neoplasms; Measures; Methods; Modeling; Modernization; Morbidity - disease rate; New Agents; Newly Diagnosed; Oncology; Outcome; Parameter Estimation; Patient Care; Patient risk; Patients; Phase III Clinical Trials; Population-Based Registry; Positron-Emission Tomography; Premature Mortality; Preparation; Probability; Progression-Free Survivals; Quality-Adjusted Life Years; Radiation; Registries; Relapse; Rest; Risk Factors; Salvage Therapy; Scientist; Seminal; Source; Standardization; Statistical Models; Survivors; Therapeutic; Time; Toxic effect; Treatment Factor; Visualization; alternative treatment; chemotherapy; clinical decision-making; clinical predictive model; cohort; cost; data dictionary; data modeling; data repository; design; disorder control; disorder risk; disorder subtype; follow-up; health care service utilization; health related quality of life; improved outcome; individual patient; innovation; interest; models and simulation; mortality; multidisciplinary; neoplasm registry; novel; novel therapeutics; open source; optimal treatments; point of care; predictive modeling; prospective; response; simulation; success; survivorship; therapy adverse effect; translational model; treatment effect

ABSTRACT Hodgkin lymphoma (HL) is associated with excellent cure rates. Despite early disease control, this success comes at considerable cost, with treatment-induced morbidity (“late effects” [LE]) that manifests as early as a year after therapy (e.g., 2nd cancers, cardiovascular [CVD] disease), compromised health-related quality of life (HRQL), and early mortality. Over the past 20 years, while important HL clinical trials have been conducted, there has been a lack of consensus about the preferred approach to treatment, considering both short- and longer term outcomes. While clinical trials provide rich information about short term disease outcomes, they do not follow efficacy beyond 5 years and rarely track post-therapy morbidity. In contrast, HL registries capture longitudinal outcomes and can be linked to other sources including healthcare utilization via administrative claims and cancer registries to characterize LE and survival. This proposal represents an unprecedented opportunity to draw on the collective expertise of International clinical HL oncology experts, epidemiologists, imaging experts, and methods & modeling experts with access to individual patient data from modern HL clinical trials and real-world registries from across the world. In preparation for the project, we harmonized individual patient data (IPD) for >12,000 HL patients from 12 clinical trials and 4 large HL registries. Building on data science principles, we established a common data model & unifying data dictionary, which allows us to expand the current database over time, incorporating future data as they become available. We will harness these multi- source data to first, create a modern prediction model via predictive modeling of pre-treatment clinical factors; second, we will estimate and validate disease progression and early emergent late effects, enhanced by the addition of interim PET imaging data and alternative treatment options via multi-state Cox proportional hazards models to estimate transition probabilities; and third, we will generate a dynamic decision model to project precalculated outcomes of interest, such as short term and longer term outcomes, including HRQL, The aggregated, “standardized” precalculated visualizations/projections (life expectance and quality-adjusted life years) will provide a basis for comparing alternative treatments for a broad range of disease subgroups and patient characteristics “on the fly.” Furthermore, both the results of the statistical models & parameter estimates from the simulation model will be validated and re-calibrated as needed in large external cohorts (i.e., German Hodgkin Study Group (GHSG) clinical trials data and Dutch HL registry). The simulation model will then be converted to open source, which will allow local analyses that go beyond our precalculated standard scenarios. Our three-step approach (predictive modeling to multi-state modeling to simulation/decision modeling) will be designed to incorporate future prospective data as new/novel treatments, and knowledge emerges over time. Overall, this project’s results will be significant as they are expected to delineate precise outcomes & optimal therapy for individual HL patients and inform the development of models and paradigms for other diseases.

抽象的 霍奇金淋巴瘤（HL）与出色的治愈率有关。尽管早期疾病控制，但这种成功 考虑到成本，以治疗引起的发病率（“后期效应” [LE]）早在 治疗后的一年（例如第二次癌症，心血管疾病），与健康相关的生活质量受损 （HRQL）和早期死亡率。在过去的20年中，尽管已经进行了重要的HL临床试验，但 考虑到短期和 长期结局。虽然临床试验提供了有关短期疾病结果的丰富信息，但它们确实 不遵循超过5年的效率，很少跟踪治疗后的发病率。相比之下，HL注册机构捕获 纵向结果，可以通过行政管理链接到其他来源，包括医疗保健利用 索赔和癌症注册表以表征LE和生存。该提议代表了一个前所未有的 有机会利用国际临床HL肿瘤专家，流行病学家的集体专业知识， 成像专家，以及具有现代HL临床访问个别患者数据的方法和建模专家 来自世界各地的试验和现实世界注册表。为了准备项目，我们协调个人 来自12项临床试验和4个大型HL注册机构的患者数据（IPD）> 12,000名HL患者。基于数据 科学原则，我们建立了一个通用的数据模型并统一数据字典，这使我们能够扩展 随着时间的推移，当前的数据库随着未来数据的可用性。我们将利用这些多 首先，通过预测临床因素的预测建模来创建现代预测模型； 其次，我们将估计并验证疾病的进展和早期的后期影响，并增强了 通过多状态COX比例危害添加临时宠物成像数据和替代治疗选择 估计过渡可能性的模型；第三，我们将生成一个动态决策模型来投影 预先计算的感兴趣结果，例如短期和长期结局，包括HRQL， 汇总，“标准化”的预先估计的可视化/预测（预期和质量调整的生活 年）将为比较广泛的疾病亚组和 患者特征“即时”。此外，统计模型和参数估计的结果既 从模拟模型中将根据需要在大型外部队列中验证和重新校准（即德语 霍奇金研究小组（GHSG）临床试验数据和荷兰HL注册表）。然后，模拟模型将是 转换为开源，这将允许超出我们预先计算的标准方案的本地分析。 我们的三步方法（对模拟/决策建模的多状态建模的预测建模）将是 旨在将未来的前瞻性数据纳入新的/新颖的治疗方法，并且随着时间的流逝而出现知识。 总体而言，该项目的结果将非常重要 针对单个HL患者的治疗，并为其他疾病的模型和范例提供了开发。