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

项目摘要/摘要 癌症及其治疗经常导致后遗症，这些后遗症并不是主动报道的，而是间歇性的。 评估过了。例如，与普通患者相比，癌症患者跌倒的几率更高。 人口减少，但通常只在引发时才报告下降。尽管电子病历(EMR) 数据库捕捉这些引出的报告，评估是间歇性的，它们的间隔经常重叠， 例如，如果病人在一个月的两次门诊中被问到“你在最近三个月内摔倒过吗？” 分开。然而，当前用于分析事件计数的方法在准确的事件时间为 未知(“间隔计数数据”)要求评估间隔不重叠。本项目致力于 这一关键差距是通过开发贝叶斯统计方法和软件来分析间隔计数数据的 时间间隔重叠，如秋季报告和其他间歇性评估的电子病历数据引起的。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推算完整的事件历史 (包括事件时间)，其他步骤可以应用广泛的模型工具包，以便更全面地观察 重复事件数据。在目标1中，我们将开发和应用贝叶斯数据增强来间歇性地 评估了泊松过程之后的复发事件。事件历史记录将使用专门的 拒绝采样器在我们的数据设置中针对高计算效率进行了优化。在目标2中，我们将开发和 对更新过程后间歇性评估的经常性事件应用贝叶斯数据增强。 事件历史将使用带有专门扰动建议的随机游走采样器进行推算。这个 这两种方法的性能将通过模拟研究进行评估，并将使用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危险因素 使用开发的统计方法从NCCN综合癌症中心获得的数据。通过我们的 提出的贝叶斯数据增强方法和开发的软件，将为本项目提供独特的 功能强大的创新工具，通常可集成临床、人口统计和经常性结果数据 记录在电子病历数据库中，以评估发病率和风险，从而进行风险分层干预。用于的工具 复发事件虽然最初是为了解决癌症患者和幸存者的跌倒问题，但它将 广泛适用于患者报告的其他类型的后遗症，如恶心和 疼痛和其他与健康相关的领域，在EMR数据库中收集反复发生的事件数据。

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.