Dynamic Prediction of Time to Next Failure Event

动态预测下一次故障事件发生的时间

• 批准号: 1612965
• 负责人: Xuelin Huang
• 金额: $ 18万
• 依托单位: University of Texas, M.D. Anderson Cancer Center
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1612965&HistoricalAwards=false
• 关键词: Dynamic; Prediction; Time; Next; Failure

In manufacturing and industrial applications, it is critical to regularly inspect all the components of a working system, record data during the inspection, use these data to predict the next potential failure event, and take preventive actions. In medical practice and research, the health status of patients is measured repeatedly over time during post-treatment follow-up visits. During each visit, new information is obtained and physicians use that information to predict a patient's prognosis and design an appropriate treatment plan. These applications involve the use of current information to predict the time until the next failure event (such as disease progression). These continuously updated predictions, called dynamic predictions, are critical for patients with non-curable diseases such as cancer or AIDS. This project will develop and apply modern statistical techniques to extract useful features from massive data sets collected over time, and then use these features to conduct predictions as accurately as possible. When these statistical methods are built into a computer software program, they can be used online to conduct predictions. Patients and physicians can use such programs to evaluate disease progression and to make early decisions about treatment and prevention. Industrial engineers can use such programs to forecast a potential system failure and initiate maintenance. Commercial web sites can collect customers' reaction data online and then apply such methods to better predict customers' needs and improve sales and customer satisfaction.Many statistical methods assume that longitudinal data trajectories follow parametric models, linear or nonlinear. However, the pattern of longitudinal data trajectories differs in each specific setting, making it difficult to identify a satisfactory parametric family that is suitable for all situations. Based on this consideration, a functional principal component analysis (FPCA) approach is used to capture the longitudinal data structures and functional patterns. The first goal of this project is to decompose biomarker trajectories into some feature functions, and then incorporate these features as covariates in the Cox proportional hazards model to make dynamic predictions. Given that the proportional hazards assumption may be too restrictive in some cases, the second goal of this project is to conduct dynamic prediction for the quantile functions of the residual event time under a flexible framework. The residual lifetime quantile regression model facilitates a meaningful interpretation and offers more direct answers than the Cox model. The third goal of this project is to develop analytic and visualization tools for identifying longitudinal data trajectory patterns prior to a failure event by looking at them backwards in time and aligning them with the failure events. Discerning these patterns can greatly facilitate dynamic prediction of the imminent failure event. The proposed methods are specially designed to handle the complications of censored data, irregular follow-up times and dynamically collected data to facilitate prediction over a range of time points.

在制造和工业应用中，定期检查工作系统的所有组件，记录检查过程中的数据，使用这些数据来预测下一个潜在的故障事件，并采取预防措施是至关重要的。在医疗实践和研究中，在治疗后随访期间反复测量患者的健康状况。在每次访问中，获得新的信息，医生使用这些信息来预测患者的预后并设计适当的治疗计划。这些应用包括使用当前信息来预测到下一个故障事件（如疾病进展）的时间。这些不断更新的预测被称为动态预测，对癌症或艾滋病等无法治愈的疾病的患者至关重要。该项目将开发和应用现代统计技术，从长期收集的大量数据集中提取有用的特征，然后利用这些特征进行尽可能准确的预测。当这些统计方法被编入计算机软件程序后，它们就可以用于在线进行预测。患者和医生可以使用这些程序来评估疾病进展，并对治疗和预防做出早期决定。工业工程师可以使用这些程序来预测潜在的系统故障并启动维护。商业网站可以在线收集客户的反应数据，然后应用这些方法更好地预测客户的需求，提高销售和客户满意度。许多统计方法假设纵向数据轨迹遵循参数模型，线性或非线性。然而，纵向数据轨迹的模式在每个特定环境中是不同的，因此很难确定适合所有情况的令人满意的参数族。基于这种考虑，使用功能主成分分析（FPCA）方法来捕获纵向数据结构和功能模式。该项目的第一个目标是将生物标志物轨迹分解为一些特征函数，然后将这些特征作为协变量纳入Cox比例风险模型中进行动态预测。考虑到比例风险假设在某些情况下可能过于严格，本项目的第二个目标是在灵活的框架下对剩余事件时间的分位数函数进行动态预测。剩余寿命分位数回归模型有助于有意义的解释，并提供比Cox模型更直接的答案。该项目的第三个目标是开发分析和可视化工具，通过及时向后查看并将其与故障事件对齐来识别故障事件之前的纵向数据轨迹模式。识别这些模式可以极大地促进对即将发生的失效事件的动态预测。所提出的方法专门用于处理截尾数据、不规则跟踪时间和动态收集数据的复杂性，以便于在一个时间点范围内进行预测。