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.

项目概要/摘要 癌症及其治疗经常会导致后遗症，这些后遗症不会主动报告，而是间歇性地报告 例如，与一般人相比，癌症患者的跌倒率更高。 尽管有电子病历 (EMR)，但跌倒通常仅在引发时才会报告。 数据库捕获这些引发的报告，评估是间歇性的，并且它们的时间间隔经常重叠， 例如，如果患者在一个月两次门诊就诊时被问到“您在过去三个月内是否跌倒过” 然而，当确切的事件时间是时，当前用于分析间隔内的事件计数的方法。 未知（“间隔计数数据”）要求评估间隔不重叠。 通过开发贝叶斯统计方法和软件来分析间隔计数数据来弥补这一关键差距 重叠间隔，如秋季报告和其他间歇评估的 EMR 数据所产生的。 应用吉布斯采样器，其中一步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），其他步骤可以应用广泛的模型工具包以进行更全面的观察 在目标 1 中，我们将开发并应用间歇性的贝叶斯数据增强。 遵循泊松过程评估的重复事件将使用专门的方法进行估算。 在目标 2 中，我们将开发和优化拒绝采样器以实现数据设置中的高计算效率。 将贝叶斯数据增强应用于更新过程后间歇性评估的重复事件。 将使用具有专门扰动建议的随机游走采样器来估算事件历史。 两种方法的性能将通过模拟研究进行评估，并且将使用 R 软件包 开发并分发给 CRAN 在这两个目标中，我们将通过 EMR 评估跌倒的发生率和风险因素。 数据来自 NCCN 综合癌症中心，使用我们开发的统计方法。 提出的贝叶斯数据增强方法和开发的软件，该项目将提供独特的 功能强大且创新的工具，可以像平常一样整合临床、人口统计和经常性结果数据 记录在 EMR 数据库中以评估发病率和风险，从而允许进行风险分层干预措施。 反复发生的事件虽然最初是为了解决癌症患者和幸存者的跌倒问题，但 广泛适用于患者报告的其他类型的后遗症，例如恶心和呕吐的发生 疼痛和其他健康相关领域，在 EMR 数据库中收集重复事件数据。