Comparative Effectiveness of Cancer Research: Use Data from Multiple Sources

癌症研究的比较有效性：使用多个来源的数据

• 批准号: 9027966
• 负责人: JING NING
• 金额: $ 29.28万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-01 至 2020-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9027966
• 关键词: Accounting; Address; Anthracyclines; Attention; Breast Cancer Patient; Breast Cancer Treatment; Cancer Center; Chronic Disease; Clinical Trials; Collaborations; Communities; Computer software; Data; Data Aggregation; Data Set; Data Sources; Databases; Disease; Epidermal Growth Factor Receptor; Evidence based treatment; Guidelines; Health; Hormone Receptor; Human; Investigation; Link; Measures; Medical Oncologist; Methods; Modeling; Observational Study; Outcome; Patient Care; Patients; Performance; Population-Based Registry; Procedures; Randomized Clinical Trials; Rare Diseases; Registries; Research; Resources; Risk; Risk Factors; Sample Size; Sampling Errors; Source; Statistical Methods; Surgeon; Survival Rate; Testing; Time; Tumor Biology; Tumor Subtype; Uncertainty; Variant; anticancer research; base; breast cancer diagnosis; chemotherapy; cohort; comparative effectiveness; cost effective; disorder subtype; effectiveness research; improved; individual patient; inflammatory breast cancer; malignant breast neoplasm; molecular subtypes; mortality; neoplasm registry; novel; oncology; outcome forecast; population based; predictive modeling; prognostic; prospective; public health relevance; semiparametric; statistics; tool; tumor; user friendly software

 DESCRIPTION (provided by applicant): Although comparative effectiveness research (CER) in oncology has attracted substantial attention to provide timely treatment comparisons and improve health outcomes, considerable methodological gaps remain for utilizing multiple sources of data together with efficient statistical methods to assemble evidence in CER. The proposed study is directly motivated by our collaborations with breast cancer medical oncologists and surgeons in the investigation of inflammatory breast cancer (IBC), a rare but aggressive form of breast cancer. The primary objective of this proposal is to develop statistical methods and risk prediction models by combining cohort data containing detailed tumor biology variables with aggregate information with or without sampling error from population-based registry databases. In this project, (Aim 1) we propose statistical methods to utilize aggregate information from external data when analyzing primary cohort data with individual patient level data under both parametric and semiparametric models for survival data, and to provide a test procedure to evaluate the comparability of the information from primary cohort data and that from external data. We will further generalize the approaches to account for uncertainty of the aggregate information in the estimation and inference procedures for survival data (Aim 2). Furthermore, (Aim 3) we will link the primary cohort data with detailed risk profiles to external data without detailed risk factors to develop a novel comprehensive IBC-specific mortality risk prediction model, and provide an estimating approach to evaluate the performance of the established risk prediction model. From an application perspective, our proposed methods of maximizing the use of existing IBC cohort data by combining them with external registry databases is cost-effective and may directly improve evidence-based treatment guidelines for IBC patients. Although motivated by IBC research, the statistical methods will be useful for addressing the challenges of CER in any chronic disease, especially for rare diseases. All software for analytical and statistical tools developed in this project, once validated, will be made available to the broader research community.

 描述（由适用提供）：尽管肿瘤学中的比较有效性研究（CER）引起了极大的关注，以提供及时的治疗比较并改善健康结果，但仍有相当大的方法差异用于利用多个数据源以及有效的统计方法以及有效的统计方法来在CER中收集证据。拟议的研究是由我们与乳腺癌医学肿瘤学家和外科医生合作进行炎症性乳腺癌（IBC）的合作，这是一种罕见但具有侵略性的乳腺癌形式。该提案的主要目的是通过将包含详细肿瘤生物学变量的队列数据与汇总信息相结合，开发统计方法和风险预测模型。在此项目中，（目标1）我们提出统计方法，以在参数和半摩擦学模型下与单个患者级别数据进行分析，以利用外部数据的总体信息，以用于生存数据，并提供测试程序，以评估来自主要队列数据的信息的兼容性。我们将进一步概括一下在生存数据的估计和推理程序中总计信息不确定性的方法（AIM 2）。此外，（AIM 3）我们将将主要队列数据与详细的风险概况与外部数据联系起来，而无需详细的风险因素开发新的全面的IBC特异性死亡率风险预测模型，并提供了一种估算方法来评估已建立风险预测模型的性能。从应用程序的角度来看，通过将现有IBC队列数据与外部注册表数据库结合使用，我们提出的最大化使用IBC队列数据的方法具有成本效益，并且可以直接改善IBC患者的基于证据的治疗指南。尽管由IBC研究激发，但统计方法将有助于解决任何慢性疾病中CER的挑战，尤其是对于罕见疾病。该项目中开发的所有用于分析和统计工具的软件（一旦经过验证）将提供给更广泛的研究社区。