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.

项目摘要/摘要 近年来，卫生保健系统和医生已齐心协力练习循证 在选择医疗时，药物并为患者提供最佳的可用信息 关心。但是，医疗决策通常很复杂，有许多不确定性和潜在的结果 考虑一下，有些有益且有些不利。一种流行的分析方法，用于识别最佳治疗 考虑不确定性的策略是决策分析，这通常涉及计算机建模 以决策树的形式概述了治疗选择 选择的结果是做出的。复杂的决策树是通过蒙特卡洛微仿真评估的 允许单个患者特征的变异性，并追踪患者的路径穿过树木；什么时候 重复微观仿真以模拟许多个体，它提供了每个人的概率 最初的决策产生的潜在结果。从这种概率分布，定量措施 与每个决定相关 测量伯恩）等；此外，成本效益还包括成本效益 可以进行分析（CEA）以计算每个选项的增量成本效益。在这个 提案，我们描述了将功能添加到数学建模软件伯克利·麦当娜（Berkeley Madonna）的计划 允许用户构建决策树并进行蒙特卡洛微观模拟和马尔可夫队列分析。 伯克利麦当娜（Berkeley Madonna）的界面旨在使数学建模快速简便，以实现非技术性 用户使用简单的语法和图形图像来构建复杂的微分方程。我们将 利用此易于使用的界面使医学研究人员能够使用软件进行微观仿真 与当前可用的选项相比，更具用户友好，透明，功能和负担得起。在AIM 1中，我们 建议进一步开发我们的决策分析用户界面，该界面允许用户以图形方式构建 决策树并执行微观模拟。在此目标中，除了优化工具和功能 GUI，我们将添加CEA输出报告和图形，灵敏度分析功能以及Markov队列分析 功能。我们将创建教程和用户指南以及现成的模板，为用户提供一个 跳出点快速制作自己的模型。在AIM 2中，我们建议优化性能代码 在单个CPU，多个CPU和GPU上。分析速度很重要，因为大型，复杂的模型可以 需要数周到几个月的时间来运行当前可用的软件，这都没有利用GPU的力量 技术;成功完成此目标将使伯克利麦当娜成为最快的软件 进行决策分析微模拟的范围很远。最后，我们将进行广泛的Beta测试。 实现这些目标将为易于使用，透明，有力和负担得起的工具提供 生物医学研究人员，教育者和专业人士，并积极影响科学发现。