Quantitative Modeling Software with Applications to Medical Decision Making

定量建模软件在医疗决策中的应用

• 批准号: 10823037
• 负责人: Smita Nayak
• 金额: $ 42万
• 依托单位: BERKELEY MADONNA, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10823037
• 关键词: Acceleration; Achievement; Adjusted Life Years; Adverse event; Caring; Characteristics; Clinical Trials; Code; Cohort Analysis; Communities; Complex; Computer Models; Computer software; Cost Effectiveness Analysis; Coupled; Decision Analysis; Decision Making; Decision Modeling; Decision Trees; Development; Differential Equation; Disease; Educational process of instructing; Effectiveness; Elements; Event; Evidence Based Medicine; Evidence based practice; Explosion; Face; Functional disorder; Goals; Graph; Growth; Health; Health Care Costs; Healthcare; Healthcare Systems; Human; Image; Individual; Industry; Intervention; Intuition; Knowledge; Letters; Link; Mathematics; Measures; Medical; Medical Research; Methods; Modeling; Outcome; Output; Patients; Performance; Phase; Physicians; Physiology; Probability; Publishing; Pythons; Quality-Adjusted Life Years; Reporting; Research; Research Personnel; Running; Small Business Innovation Research Grant; Speed; System; Technology; Testing; Time; Trees; Uncertainty; Work; analytical method; burden of illness; cohort; computerized tools; cost; design; experience; flexibility; graphical user interface; health assessment; incremental cost-effectiveness; individual patient; interest; mathematical model; model building; multithreading; parallelization; portability; programs; recruit; relative cost; simulation; simulation software; stem; success; syntax; tool; treatment choice; treatment strategy; user-friendly

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.

项目总结/摘要 近年来，卫生保健系统和医生共同努力，实践循证医学。 药物，并为患者提供最好的信息时，作出选择，他们的医疗 在乎然而，医疗决策往往是复杂的，有许多不确定性和潜在的结果， 有些是有利的，有些是不利的。一种用于帮助确定最佳治疗的流行分析方法 决策分析是一种考虑不确定性的决策分析，它通常涉及计算机建模， 以决策树的形式概述的治疗选择，其中显示可能的选择和健康结果 这是由于所做的选择。复杂的决策树通过蒙特卡洛微观模拟进行评估， 允许个体患者特征的可变性，并通过树跟踪患者的路径;当 微观模拟重复多次以模拟许多个体，它提供每个个体的概率。 最初决定的潜在结果。从这个概率分布，定量测量 可以计算与每个决策相关联的生命年数，例如生命年数、质量调整生命年数（通用的 疾病负担的措施），以及其他;此外，当成本也被纳入，成本效益 可以进行成本效益分析（CEA）来计算每个选项的增量成本效益。在这 建议，我们描述了计划增加功能的数学建模软件伯克利麦当娜， 允许用户建立决策树，并进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设计，使数学建模快速和容易的非技术 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。我们将 利用这个易于使用的界面，使医学研究人员能够使用软件进行微观模拟， 比目前可用的选项更用户友好、透明、强大和实惠。目标1： 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 决策树和执行微观模拟。在这个目标中，除了优化工具和功能， GUI，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 能力的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。分析速度非常重要，因为大型复杂模型可以 使用当前可用的软件运行需要数周至数月的时间，其中没有一个软件利用GPU的强大功能 技术;这一目标的成功完成将使伯克利麦当娜最快的可用软件， 远用于执行决策分析微观模拟。最后，我们将进行广泛的Beta测试。 这些目标的实现将提供一个易于使用、透明、功能强大且价格合理的工具， 生物医学研究人员，教育工作者和专业人士，并积极影响科学发现。