Quantitative Modeling Software with Applications to Medical Decision Making

项目摘要/摘要 近年来，医疗保健系统和医生共同努力实践循证医学 药物，并为患者提供最佳的可用信息，当他们选择自己的医疗 关心。然而，医疗决策往往是复杂的，具有许多不确定性和潜在的后果 考虑一下，有些是有益的，有些是不利的。一种流行的分析方法，用于帮助确定最佳治疗方法 考虑不确定性的策略是决策分析，它通常涉及计算机建模 以决策树的形式概述的治疗选择，其中显示了可能 因为所做的选择而发生。通过蒙特卡罗微观模拟对复杂决策树进行评估 允许单个患者特征的可变性，并在树中跟踪患者的路径；当 微模拟被重复多次以模拟多个个体，它提供了每个个体的概率 最初的决定可能产生的结果。从这个概率分布来看，量化措施 可以计算与每个决策相关联的寿命年、质量调整后的寿命年(一般 疾病负担的衡量标准)，以及其他；此外，如果还包括成本，则成本效益 可以进行分析(CEA)来计算每个备选方案的增量成本效益。在这 提案中，我们描述了向数学建模软件Berkeley Madonna添加功能的计划 允许用户构建决策树并执行蒙特卡罗微观模拟和马尔可夫队列分析。 伯克利·麦当娜的界面旨在让非技术人员快速、轻松地进行数学建模 用户通过使用简单的语法和图形图像来构造复杂的微分方程式。我们会 利用这一易于使用的界面，医学研究人员可以使用以下软件执行微模拟 比目前可用的选项更友好、更透明、更强大、更实惠。在目标1中，我们 建议进一步开发我们的决策分析用户界面，允许用户以图形方式构建 决策树并执行微观模拟。在这一目标中，除了优化用于 图形用户界面，我们将添加CEA输出报告和图形、敏感性分析功能和马尔可夫队列分析 能力。我们将创建教程和用户指南以及现成的模板，为用户提供 快速制作自己的模型的起点。在目标2中，我们建议优化代码以提高性能 在单CPU、多CPU和GPU上。分析速度很重要，因为大型、复杂的模型 用目前可用的软件运行需要几周到几个月的时间，这些软件都不能利用GPU的能力 技术；成功完成这一目标将使伯克利麦当娜成为最快的可用软件 用于执行决策分析微模拟的FAR。最后，我们将进行广泛的Beta测试。 实现这些目标将提供一种易于使用、透明、功能强大且经济实惠的工具 生物医学研究人员、教育工作者和专业人员，并对科学发现产生积极影响。

Project Summary/Abstract In recent years, health care systems and physicians have made concerted efforts to practice evidence-based medicine and provide patients with the best available information when making choices about their medical care. However, medical decisions are often complex with many uncertainties and potential outcomes to consider, some beneficial and some adverse. A popular analytic method used to help identify best treatment strategies while accounting for uncertainty is decision analysis, which typically involves computer modeling of a treatment choice outlined in the form of a decision tree, which shows options and health outcomes that may occur as a result of the choice made. Complex decision trees are evaluated via Monte Carlo microsimulation to allow for variability in individual patient characteristics and trace a patient’s path through the tree; when the microsimulation is repeated many times to simulate many individuals, it provides the probability of each potential outcome resulting from the initial decision. From this probability distribution, quantitative measures associated with each decision can be calculated such as life years, quality-adjusted life years (a generic measure of disease burden), and others; furthermore, when costs are also incorporated, cost-effectiveness analysis (CEA) can be performed to compute the incremental cost-effectiveness of each option. In this proposal, we describe plans to add functionality to the mathematical modeling software Berkeley Madonna to allow users to build decision trees and carry out Monte Carlo microsimulations and Markov cohort analysis. Berkeley Madonna’s interface was designed to make mathematical modeling quick and easy for non-technical users by using a simple syntax and graphical images to construct sophisticated differential equations. We will leverage this easy-to-use interface to enable medical researchers to perform microsimulation with software that is more user-friendly, transparent, powerful, and affordable than currently available options. In Aim 1, we propose further development of our decision analysis user interface that allows users to graphically construct decision trees and perform microsimulations. In this aim, in addition to optimizing tools and features for the GUI, we will add CEA output reports and graphics, sensitivity analysis capabilities, and Markov cohort analysis capabilities. We will create tutorials and a user guide as well as ready-made templates that provide users a jumping off point for quickly making their own models. In Aim 2, we propose to optimize code for performance on single CPUs, multiple CPUs, and GPUs. Analysis speed is important because large, complex models can take weeks to months to run with currently available software, none of which harness the power of GPU technology; successful completion of this aim would make Berkeley Madonna the fastest available software by far for performing decision analysis microsimulations. Finally, we will carry out extensive beta testing. Achievement of these goals will provide an easy-to-use, transparent, powerful, and affordable tool to biomedical researchers, educators, and professionals, and positively impact scientific discovery.