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: 10574255
• 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/10574255
• 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.

项目概要/摘要 在晚期癌症的现代治疗中，许多患者使用不同的药物进行多轮治疗。 药剂。尽管治疗转换在临床实践中很常见，但其成本效益 治疗顺序尚未得到充分研究。多线治疗的有效且准确的建模仍然存在 一个开放的问题。常用的马尔可夫模型是无记忆的，这在研究多个模型时会出现问题 治疗线，因为该模型无法轻松处理患者内部的依赖性。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（即 也就是说，给出了哪些代理以及以什么顺序）。从电子设备中提取的真实世界患者信息 病历 (EMR) 为更好地了解实践中的治疗结果提供了新的机会。我们 可以使用 EMR 数据创建改进的健康状态模型，该模型综合患者级别的数据，已发表 临床试验的结果以及文献中的健康效用（生活质量）估计。在此，我们建议 通过将真实世界数据纳入微观模拟模型来研究治疗顺序。这些状态转换 模型使用蒙特卡罗方法来模拟不同健康状态下的个体路径。我们的模型将 特别关注正确解释患者体内事件随时间的依赖性。患者层面 肿瘤生物学、年龄和合并症等变量将影响所有方面的治疗结果 疗法，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 使用多个参数模型来拟合基于 EMR 的数据。这些转移概率将被用于 微观模拟模型，还包括医疗费用和生活质量等辅助信息 调整（公用事业）。我们还将使用引导重采样开发一种完全非参数的方法 患者群体，其中每个人通过模型状态的路径将被直接使用。再次，成本和 每个健康州都将纳入公用事业。在模拟研究中，我们将评估每个的性能 模型与传统的马尔可夫模型方法相比。最后我们来说明一下这个方法的应用 使用 Flatiron Health 创建的 EMR 衍生数据集研究晚期尿路上皮癌的方法。 该数据集包含有关治疗和结果的大量数据，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 成本和生活质量没有被捕获）。我们将演示如何使用我们的方法来估计 一线卡铂为基础的治疗（与一线顺铂为基础的治疗相比）的成本效益 二线免疫检查点抑制剂，我们将比较不同策略的结果。