Modeling Multi-Source Data in Hodgkin Lymphoma

摘要 霍奇金淋巴瘤(HL)具有极高的治愈率。尽管疾病得到了早期控制，但这一成功 要付出相当大的代价，治疗引起的发病率(“后遗症”)最早会在 治疗后一年(如第二次癌症、心血管疾病)，与健康相关的生活质量下降 (HRQL)和早期死亡率。在过去的20年里，在进行了重要的HL临床试验的同时， 对于首选的治疗方法一直缺乏共识，考虑到短期和长期的 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，但它们确实提供了 不跟踪疗效超过5年，很少跟踪治疗后的发病率。相比之下，HL注册处捕获 纵向结果，并可通过管理链接到其他来源，包括医疗保健利用率 索赔和癌症登记，以表征LE和存活率。这项提议是史无前例的 利用国际临床HL肿瘤学专家、流行病学家、 影像专家、方法和建模专家，可访问现代HL临床的个别患者数据 来自世界各地的审判和真实世界的登记。在为项目做准备的过程中，我们协调了个人 来自12个临床试验和4个大型HL注册的12,000名HL患者的患者数据(IPD)。以数据为基础 科学原理，我们建立了一个通用的数据模型&统一数据字典，它允许我们扩展 当前数据库随着时间的推移，在未来数据变得可用时纳入这些数据。我们将利用这些多方面的- 数据来源：首先，通过对治疗前临床因素的预测建模，建立现代预测模型； 其次，我们将评估和验证疾病进展和早期紧急后遗症效应，这一点通过 通过多状态COX比例风险增加临时PET成像数据和替代治疗选择 用于估计转移概率的模型；第三，我们将生成动态决策模型以进行项目 预先计算的感兴趣的结果，如短期和长期结果，包括HRQL、 聚合的“标准化”预先计算的可视化/预测(预期寿命和质量调整后的寿命 年)将为比较广泛的疾病亚群的替代疗法和 病人的特征“在路上”。此外，统计模型和参数估计的结果 将根据需要在大型外部队列(即德国)中验证和重新校准来自模拟模型的 霍奇金研究小组(GHSG)临床试验数据和荷兰HL注册)。然后将模拟模型 转换为开放源代码，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(从预测建模到多状态建模再到模拟/决策建模)将是 旨在将未来的预期数据纳入新的/新颖的治疗方法，随着时间的推移，知识会不断涌现。 总体而言，该项目的结果将是重要的，因为它们有望勾勒出准确的结果&最佳 为个别HL患者的治疗提供信息，并为其他疾病的模型和范例的发展提供信息。

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.