METHODOLOGIC RESEARCH IN SIMULATING COMPLEX DISEASE

该项目的目的是开发和提炼新的量化 这些技术将增强我们预测最佳时间的能力 终末期肝病的肝移植。我们打算 通过在应用程序中进行方法学研究来实现这一目标 基于蒙特卡罗方法的复杂马尔可夫过程评价 模拟模型。 正在研究的临床问题是一个重要的问题。相当可观 ESLD的治疗每年都消耗资源，它的排名 第七，作为疾病相关死亡的原因，并产生大量 当患者出现腹水时，发病率和生活质量会下降， 脑病或出血性静脉曲张。移植是一种有效的但 NIH肝脏共识会议对ESLD的昂贵治疗 器官移植被公认为“延长生命的良方 对于患有几种肝病的患者来说，质量是很重要的。 AHCPR对肝移植推荐移植的评价 用于原发性胆汁性肝硬变或硬化性胆管炎引起的ESLD。 然而，肝移植的时机选择是一个重要的临床问题。 这个问题在文献中很少受到关注。 移植过晚会增加围手术期风险并降低 存活；过早移植会暴露出许多可能患有 通向不必要风险的良性路线。这两个计时误差都浪费了稀缺的 资源。 时机决策为方法论研究提供了肥沃的土壤 慢性疾病的技术干预的一般性问题。为 出于伦理和务实的原因，不太可能随机控制 对最佳移植时机的试验将永远不会发生。 因此，观测和决策分析技术提供了 用于检查此问题的适当工具。然而，马尔可夫过程， 用于对疾病建模的最常见的决策分析技术 哪些事件在时间上传播，无法有效地建模 复杂的自然历史。从技术上讲，这是因为过渡 标准过程中的概率可能不依赖于路径。这 需要创建规模无法管理的模型，通常涉及 成千上万个不同的州。 为了解决路径依赖问题，这项工作将继续研究 将“记忆”融入决策的蒙特卡罗评估技术 基于马尔可夫过程的分析模型。它将构建 对ESLD自然历史的定量描述对于 校准这些模型，并将在以下背景下评估这些模型 移植的最佳时机问题。最后，因为 决策分析模型很容易纳入成本，这项工作将 调查各种移植选择和移植的经济影响 计时标准。生存和成本是直接联系在一起的，而时机 决策影响生存和花费在ESLD上的资源。

The purpose of this project is to develop and refine new quantitative techniques that will enhance our ability to predict the optimal timing of liver transplantation in end stage liver disease (ESLD). We intend to accomplish this goal by conducting methodologic research in the application of Monte Carlo methods to the evaluation of complex Markov process-based simulation models. The clinical issue being studied is an important one. Substantial resources are consumed each year in the treatment of ESLD, which ranks seventh as a cause of disease-related death, and produces substantial morbidity and decreases quality of life when patients develop ascites, encephalopathy or bleeding varices. Transplantation is an effective but expensive therapy for ESLD, which the NIH consensus Conference on Liver Transplantation recognized as providing "prolongation of life of good quality" for patients with several types of liver disease. More recently, the AHCPR evaluation of liver transplantation recommends transplantation for ESLD from primary biliary cirrhosis or sclerosing cholangitis. However, the timing of liver transplantation is an important clinical question that has received little attention in the literature. Transplanting too late increases perioperative risks and decreases survival; transplanting too early exposes many patients who may have a benign course to unnecessary risks. Both timing errors waste a scarce resource. The timing decision provides fertile ground for methodologic research in the generic problem of technologic intervention in chronic disease. For ethical and pragmatic reasons it is unlikely that a randomized controlled trial of the optimal timing of transplantation would ever take place. Therefore, observational and decision analytic techniques provide appropriate tools for examining this problem. However, Markov processes, the most common decision analytic technique used to model diseases for which events are spread out in time, are unable to effectively model complex natural histories. Technically, this is because transition probabilities in standard processes may not be path dependent. This necessitates the creation of models of unmanageable size, often involving thousands of separate states. To solve the path dependence problem, this work will continue research on Monte Carlo evaluation techniques that incorporate "memory" into decision analysis models based on Markov processes. It will construct the quantitative descriptions of the natural history of ESLD necessary to calibrate such models, and it will evaluate these models in the context of the problem of the optimal timing of transplantation. Finally, because decision analysis models easily incorporate costs, this work will investigate the economic impact of various transplantation selection and timing criteria. Survival and costs are directly linked, and the timing decision impacts both survival and resources spent on ESLD.