Modeling Multi-Source Data in Hodgkin Lymphoma

摘要 霍奇金淋巴瘤（HL）的治愈率非常高。尽管早期的疾病控制， 治疗的成本相当高，治疗诱导的发病率（“迟发效应”[LE]）早在 治疗后一年（例如，第二类癌症、心血管[CVD]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年里，虽然进行了重要的HL临床试验， 关于首选的治疗方法缺乏共识，考虑到短期和长期的治疗方法， 更长期的结果。虽然临床试验提供了关于短期疾病结果的丰富信息， 5年后疗效未随访，很少跟踪治疗后发病率。相比之下，HL登记研究捕获了 纵向结果，并可通过行政管理链接到其他来源，包括医疗保健利用 索赔和癌症登记，以表征LE和生存。这一提议是前所未有的 有机会利用国际临床HL肿瘤学专家，流行病学家， 成像专家和方法与建模专家，可访问现代HL临床研究中的个体患者数据， 来自世界各地的临床试验和真实世界的登记。在准备项目时，我们协调了个人 来自12项临床试验和4项大型HL登记研究的> 12，000例HL患者的患者数据（IPD）。以数据为基础 科学的原则，我们建立了一个通用的数据模型和统一的数据字典，这使得我们可以扩展 随着时间的推移，目前的数据库，并纳入未来的数据，因为他们成为可用的。我们将利用这些多- 源数据，首先，通过治疗前临床因素的预测建模来创建现代预测模型; 其次，我们将评估和验证疾病进展和早期出现的晚期效应， 通过多状态考克斯比例风险增加中期PET成像数据和替代治疗选择 模型来估计转移概率;第三，我们将生成一个动态决策模型来预测 预先计算的利益结果，如短期和长期结果，包括HRQL， 汇总的、“标准化的”预先计算的可视化/预测（预期寿命和质量调整寿命 年）将为比较广泛疾病亚组的替代治疗提供基础， 病人的特征“在飞行中”。此外，统计模型和参数估计的结果 将根据需要在大的外部群组中进行验证和重新校准（即，德国 霍奇金研究组（GHSG）临床试验数据和荷兰HL登记研究）。然后，仿真模型将是 转换为开源，这将允许超出我们预先计算的标准场景的本地分析。 我们的三步方法（预测建模到多状态建模到仿真/决策建模）将是 旨在将未来的前瞻性数据作为新的/新颖的治疗方法，知识随着时间的推移而出现。 总的来说，这个项目的结果将是重要的，因为他们预计将描绘精确的结果和最佳的 本研究为HL患者个体的治疗提供了指导，并为其他疾病的模型和范例的开发提供了信息。

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.