Bayesian Data Augmentation for Recurrent Events in Electronic Medical Records of Patients with Cancer

癌症患者电子病历中重复事件的贝叶斯数据增强

• 批准号: 10579304
• 负责人: Patrick M Schnell
• 金额: $ 7.38万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10579304
• 关键词: Address; Archives; Bayesian Analysis; Bayesian Method; Cancer Patient; Cessation of life; Clinical; Comprehensive Cancer Center; Computer software; Computerized Medical Record; Computing Methodologies; Data; Data Set; Databases; Dose; Event; General Population; Health; Hospitals; Incidence; Injury; Intervention; Malignant Neoplasms; Markov Chains; Markov chain Monte Carlo methodology; Measures; Methods; Modeling; National Comprehensive Cancer Network; Nausea; Ohio; Oncologist; Outcome; Outpatients; Pain; Participant; Patients; Performance; Pharmaceutical Preparations; Process; Recording of previous events; Recurrence; Reporting; Research; Resources; Risk; Risk Factors; Sample Size; Scheme; Statistical Methods; Surveys; Survivors; Symptoms; System; Time; Universities; Visit; Walking; clinical practice; experience; fall injury; fall risk; falls; health related quality of life; improved; innovation; malignant breast neoplasm; older patient; participant enrollment; patient health information; patient oriented; risk stratification; side effect; simulation; software development; tool

PROJECT SUMMARY/ABSTRACT Cancer and its treatment frequently result in sequelae that are not pro-actively reported but rather intermittently assessed. For example, patients with cancer experience a higher rate of falls compared to the general population, but falls are commonly reported only when elicited. Although electronic medical record (EMR) databases capture these elicited reports, the assessments are intermittent and their intervals often overlap, e.g., if patients are asked “have you fallen within the last three months” at two outpatient visits one month apart. However, current methods for analyzing event counts within intervals when exact event times are unknown (“interval count data”) require assessment intervals to be non-overlapping. This project addresses this critical gap by developing Bayesian statistical methods and software for analyzing interval count data with overlapping intervals, as arise from fall reports and other intermittently assessed EMR data. These methods apply a Gibbs sampler in which one step uses Bayesian data augmentation to impute full event histories (including event times) to which other steps may apply a broad toolkit of models for more fully observed recurrent event data. In Aim 1 we will develop and apply Bayesian data augmentation for intermittently assessed recurrent events following Poisson processes. Event histories will be imputed using specialized rejection samplers optimized for high computational efficiency in our data setting. In Aim 2 we will develop and apply Bayesian data augmentation for intermittently assessed recurrent events following renewal processes. Event histories will be imputed using random walk samplers with specialized perturbation proposals. The performance of both methods will be assessed via simulation study, and an R software package will be developed and distributed to CRAN. In both aims we will evaluate incidence and risk factors for falls via EMR data from an NCCN comprehensive cancer center using the developed statistical methods. Through our proposed Bayesian data augmentation approach and software developed, this project will provide uniquely capable and innovative tools to integrate clinical, demographic, and recurrent outcome data as commonly recorded in EMR databases to assess incidence and risk, allowing for risk-stratified interventions. The tools for recurrent events, though originally conceived of to address falls among patients with cancer and survivors, will be broadly applicable to both other types of patient-reported sequelae such as occurrences of nausea and pain, and other health-related fields that collect recurrent event data in EMR databases.

项目摘要/摘要 癌症及其治疗经常导致后遗症，这些后遗症未被积极报道，而是间歇性地报道的 评估。例如，癌症患者的跌倒率与一般性相比更高 人口，但通常仅在引起时才报告跌倒。虽然电子病历（EMR） 数据库捕获了这些引起的报告，评估是间歇性的，它们的间隔通常重叠， 例如，如果询问患者“过去三个月内跌倒了”，两个月的门诊就诊 除外。但是，当确切的事件时间为时，当前用于分析事件计数的方法是 未知（“间隔计数数据”）要求评估间隔是不重叠的。该项目解决 通过开发贝叶斯统计方法和软件来分析间隔计数数据，这一关键差距 秋季报告和其他间歇性评估EMR数据的重叠间隔。这些方法 应用Gibbs采样器，其中一个步骤使用贝叶斯数据增强来估算完整的事件历史 （包括事件时间）其他步骤可以在哪些步骤中应用广泛的模型工具包，以进行更充分的观察 经常性事件数据。在AIM 1中，我们将开发并应用贝叶斯数据扩展以间歇性 评估了泊松过程后的经常性事件。事件历史将使用专业归为 在我们的数据设置中，拒绝样品针对高计算效率进行了优化。在AIM 2中，我们将发展并 在更新过程后，将贝叶斯数据增强应用于间歇性评估的经常性事件。 事件历史将使用带有专门扰动建议的随机步行样品归纳。这 两种方法的性能将通过仿真研究评估，R软件包将是 开发并分发给克兰。在这两个目标中，我们都将通过EMR评估跌倒的事件和风险因素 使用开发的统计方法来自NCCN综合癌症中心的数据。通过我们的 拟议的贝叶斯数据增强方法和已开发的软件，该项目将提供独特的 具有综合临床，人口统计和经常性结果数据的能力和创新工具通常 记录在EMR数据库中以评估发病率和风险，从而允许风险分层的干预措施。工具 反复发生的事件，尽管最初是为了解决癌症患者和幸存者的跌倒，但 广泛适用于其他两种类型的患者报告的后遗症，例如恶心和 疼痛和其他与健康相关的领域，这些领域在EMR数据库中收集复发事件数据。