Modeling Therapy Sequence for Advanced Cancer: A Microsimulation Approach Using Real-World Data

晚期癌症治疗序列建模：使用真实世界数据的微观模拟方法

• 批准号: 10693287
• 负责人: Elizabeth Handorf
• 金额: $ 5万
• 依托单位: RESEARCH INST OF FOX CHASE CAN CTR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10693287
• 关键词: Modeling; Therapy; Sequence; Advanced; Cancer

Project Summary/Abstract In modern treatment of advanced cancers, many patients go through multiple rounds of therapy using different pharmaceutical agents. Although therapy switching is common in clinical practice, the cost-effectiveness of sequences of therapies is under-studied. Effective and accurate modeling of multiple lines of therapy remains an open problem. The often-used Markov model is memoryless, which is problematic when studying multiple lines of therapy, as the model cannot easily deal with dependence within patients. Therefore, there is an unmet and growing need to develop specialized models to evaluate the cost-effectiveness of therapy sequence (that is, which agents are given and in what order). Real-world patient information extracted from the Electronic Medical Record (EMR) provides a novel opportunity to better understand treatment outcomes in practice. We can use EMR data to create improved health-state models which synthesize patient-level data, published results from clinical trials, and health utility (quality of life) estimates from the literature. Here, we propose to study therapy sequence by incorporating real-world data in microsimulation models. These state-transition models use Monte Carlo methods to simulate individual paths through different health states. Our models will particularly focus on correctly accounting for the dependence of events over time within patients. Patient-level variables, such as tumor biology, age, and comorbid conditions, will impact treatment outcomes on all lines of therapies, thus introducing statistical dependence. We will first estimate transition probabilities between states using several parametric models fit to the EMR-based data. These transition probabilities will then be used in a microsimulation model which also includes auxiliary information including healthcare costs and quality of life adjustments (utilities). We will also develop a fully non-parametric approach using bootstrap resampling of the patient population, where each individual’s path through model states will be used directly. Again, costs and utilities will be incorporated for each health state. In simulation studies, we will assess the performance of each model compared to a traditional Markov model approach. Finally, we will illustrate the application of this method in a study of advanced urothelial carcinoma using an EMR-derived dataset created by Flatiron Health. This dataset contains extensive data on treatments and outcomes including progression events and death, however, it lacks all the measures we would need to conduct a full cost-effectiveness analysis (i.e. financial costs and quality of life are not captured). We will demonstrate how our approach can be used to estimate the cost-effectiveness of first-line carboplatin based therapy (vs. first-line cisplatin based therapy) followed by second-line immune checkpoint inhibitors, and we will compare results from the different strategies.

项目总结/摘要 在晚期癌症的现代治疗中，许多患者使用不同的药物经历多轮治疗。 药剂虽然治疗转换在临床实践中很常见，但 治疗顺序研究不足。多线治疗的有效和准确建模仍然存在 一个悬而未决的问题。常用的马尔可夫模型是无记忆的，这在研究多个 治疗线，因为该模型无法轻松处理患者的依赖性。因此，有一个未满足的 并且越来越需要开发专门的模型来评估治疗序列的成本效益（ 是，哪些代理人被给予，并以什么顺序）。从电子设备中提取的真实患者信息 医疗记录（EMR）提供了一个新的机会，以更好地了解在实践中的治疗结果。我们 可以使用EMR数据来创建改进的健康状态模型，该模型综合了患者水平的数据， 来自临床试验的结果和来自文献的健康效用（生活质量）估计。在此，我们建议 通过在微观模拟模型中结合真实世界数据来研究治疗序列。这些状态转换 模型使用蒙特卡罗方法模拟通过不同健康状态的各个路径。我们的模特将 特别是集中于正确地说明患者体内事件随时间的依赖性。患者水平 变量，如肿瘤生物学、年龄和共病状况，将影响所有治疗线的治疗结果。 治疗，从而引入统计依赖性。我们将首先估计状态之间的转移概率 使用几个参数模型来拟合基于EMR的数据。然后，这些转移概率将用于 微观模拟模型，其中还包括辅助信息，包括医疗费用和生活质量 调整（公用事业）。我们还将开发一个完全非参数的方法，使用bootstrap resolution的 患者群体，其中每个个体通过模型状态的路径将被直接使用。再次，成本和 将为每个卫生州纳入公用事业。在模拟研究中，我们将评估每一个性能 与传统的马尔可夫模型方法相比。最后，我们将举例说明这一应用。 使用Flatiron Health创建的EMR衍生数据集进行晚期尿路上皮癌研究的方法。 该数据集包含关于治疗和结局的广泛数据，包括进展事件和死亡， 然而，它缺乏我们进行全面成本效益分析所需的所有措施（即财务 生活成本和生活质量没有得到体现）。我们将演示如何使用我们的方法来估计 基于卡铂的一线治疗（与基于顺铂的一线治疗）的成本-效果，随后 二线免疫检查点抑制剂，我们将比较不同策略的结果。