Comprehensive Prognostic Modeling for Esophageal Cancer: A Bayesian Approach

食管癌的综合预后模型：贝叶斯方法

• 批准号: 7862570
• 负责人: Xi Kathy Zhou
• 金额: $ 22.56万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7862570
• 关键词: Accounting; Address; Area; Cancer Patient; Cancer Prognosis; Caring; Characteristics; Clinical; Complex; Complication; Data; Data Analyses; Data Sources; Databases; Decision Making; Diagnosis; Disease; Elderly; Ensure; Future; Goals; Health Policy; Heterogeneity; Histology; Incidence; Individual; Institution; Knowledge; Link; Malignant neoplasm of esophagus; Medicare; Methodology; Methods; Modeling; Operative Surgical Procedures; Outcome; Outcome Assessment; Patients; Performance; Physicians; Population; Prognostic Factor; Property; Provider; Quality of Care; Randomized; Registries; Research; Research Personnel; Role; Simulate; Source; Statistical Methods; Structure; Techniques; Validation; Variant; base; cancer therapy; data structure; demographics; direct application; disease characteristic; effective intervention; frailty; improved; indexing; mortality; older patient; outcome forecast; patient population; policy implication; prognostic; public health relevance; research study; simulation; socioeconomics; software development; user-friendly

DESCRIPTION (provided by applicant): Our long-term objective is to develop accurate and generalizable prognostic models using complex data sources and contribute to improve patient outcome. This study is motivated by the clinical need of developing accurate prognostic models for esophageal cancer and the methodological need of improvement in data analysis with complex data source. Esophageal cancer presents a unique set of challenges that signifies the importance of this effort. First, its relatively low incidence rate makes large randomized experiments difficult to carry out. Large observational database becomes a particularly important source for research. Second, its rising incidence and high mortality rate present an urgent need to accurately identify prognostic factors where effective interventions could be directed. However, due to limitations from either data sources or statistical approaches, the prognostic roles of histology type of the disease, variation in treatments (i.e., the extensiveness of the surgery) and quality of care (using provider volume as a surrogate) and their clinical and health policy implications are hotly debated. Third, despite the availability of advanced statistical methods, such as mixed effects models, to handle a key aspect of data complexity from large databases - the heterogeneity due to clustering - few prognostic models are built upon these methods. This is partly due to the lack of guidance on how to best model the clustered data structure. Questions regarding whether, by how much, and under which setting such advanced data structure modeling would improve the accuracy of the prognostic model remain to be answered. The goal of this study is to improve our understanding regarding the key variables in esophageal cancer prognosis and to advance our knowledge regarding the validity and importance of the advanced statistical methods in prognostic modeling using complex data sources. It will be achieved through the following specific aims: (1) To develop comprehensive prognostic models for multiple outcomes (including fatal and no-fatal short-term complications, overall and disease-specific long-term survival) following esophageal cancer treatment using SEER-Medicare database. Bayesian mixed-effects models will be used to account for the clustered data structure. The models will be validated internally and externally. (2) To evaluate methods for clustered survival data using statistical simulations. By simulating data of complex structure that mimic real studies, the operational characteristics of several commonly used frailty models for issues of importance to prognostic modeling (including the individuallevel predictor effect estimation, cluster-level predictor effect estimation, heterogeneity assessment and outcome prediction) will be investigated. The performance of the frailty model approach and other techniques including the marginal and stratified approaches will be compared with regard to accuracy in predictors' effects estimation and outcome prediction. Results from this study will have direct applicability in aiding clinical and health policy decision making for the care of elder patients with esophageal cancer. They will also impact positively on future prognostic modeling using registry, administrative, and observational databases. PUBLIC HEALTH RELEVANCE: The proposed studies aim to develop comprehensive prognostic models for esophageal cancer using SEER Medicare linked database and to compare methods for clustered survival data using statistical simulations. The new prognostic models will help to more accurately predict outcomes of esophageal cancer patients with a particular set of disease characteristics and treated with a specific treatment by physicians at a particular institution of certain quality of care. Results from the simulation study will advance our knowledge on the operational characteristics of the related advanced statistical methods and provide guidance for future prognostic modeling efforts using registry, administrative, and observational databases.

描述（由申请人提供）：我们的长期目标是利用复杂的数据源开发准确和可推广的预后模型，并有助于改善患者的预后。本研究的动机是建立准确的食管癌预后模型的临床需要，以及在复杂数据来源的数据分析中改进方法学的需要。食管癌呈现出一系列独特的挑战，这表明了这一努力的重要性。首先，其发病率相对较低，使得大型随机实验难以进行。大型观测数据库成为研究的重要来源。其次，其发病率上升和死亡率高，迫切需要准确地确定预后因素，在这些因素中可以进行有效的干预。然而，由于数据来源或统计方法的限制，疾病的组织学类型、治疗方法的变化（即手术的广泛性）和护理质量（使用提供者数量作为替代）及其临床和卫生政策影响的预后作用受到了激烈的争论。第三，尽管有先进的统计方法，如混合效应模型，可以处理大型数据库中数据复杂性的一个关键方面——聚类导致的异质性——但基于这些方法建立的预测模型很少。这在一定程度上是由于缺乏关于如何最好地为集群数据结构建模的指导。关于这种先进的数据结构建模是否、提高多少以及在何种设置下会提高预测模型的准确性的问题仍有待回答。本研究的目的是提高我们对食管癌预后的关键变量的理解，并提高我们对使用复杂数据源进行预后建模的先进统计方法的有效性和重要性的认识。具体目标如下：(1)利用SEER-Medicare数据库建立食管癌治疗后多种结局（包括致死性和非致死性短期并发症、总体生存期和疾病特异性长期生存期）的综合预后模型。贝叶斯混合效应模型将用于解释聚类数据结构。模型将在内部和外部进行验证。(2)通过统计模拟评估聚类生存数据的方法。通过模拟真实研究的复杂结构数据，研究了几种常用的脆弱性模型在预测建模中重要问题（包括个体水平预测效应估计、集群水平预测效应估计、异质性评估和结果预测）的运行特征。将比较脆弱性模型方法和其他技术的性能，包括边际和分层方法在预测者效应估计和结果预测方面的准确性。本研究结果将直接适用于老年食管癌患者的临床护理和卫生政策决策。它们还将对使用注册表、管理和观察数据库的未来预后建模产生积极影响。公共卫生相关性：拟议的研究旨在使用SEER医疗保险关联数据库建立食管癌的综合预后模型，并使用统计模拟比较聚类生存数据的方法。新的预后模型将有助于更准确地预测食管癌患者的预后，这些患者具有特定的疾病特征，并由具有一定护理质量的特定机构的医生进行特定治疗。模拟研究的结果将提高我们对相关先进统计方法的操作特征的认识，并为使用注册表、管理和观测数据库的未来预测建模工作提供指导。