Comparative Effectiveness of Cancer Research: Use Data from Multiple Sources

 描述(由申请人提供)：虽然肿瘤学中的比较有效性研究(CER)在提供及时的治疗比较和改善健康结果方面引起了极大的关注，但在CER中利用多种数据来源和有效的统计方法收集证据方面仍存在相当大的方法学差距。这项拟议的研究直接受到我们与乳腺癌内科肿瘤学家和外科医生在炎症性乳腺癌(IBC)研究中的合作的推动，IBC是一种罕见但侵袭性的乳腺癌。这项建议的主要目标是通过将包含详细肿瘤生物学变量的队列数据与来自基于人口的登记数据库的有或无抽样误差的聚集信息相结合，来开发统计方法和风险预测模型。在本项目中，(目标1)我们提出了在生存数据的参数和半参数模型下分析原始队列数据和个体患者水平数据时利用来自外部数据的聚集信息的统计方法，并提供了评估来自原始队列数据和来自外部数据的信息的可比性的测试程序。我们将进一步推广在生存数据估计和推断程序中考虑汇总信息不确定性的方法(目标2)。此外，(目标3)我们将把具有详细风险概况的主要队列数据与没有详细风险因素的外部数据联系起来，以开发一种新的全面的IBC特有的死亡风险预测模型，并提供一种评估方法来评估所建立的风险预测模型的性能。从应用的角度来看，我们提出的通过将现有IBC队列数据与外部登记数据库相结合来最大限度地利用这些数据的方法是具有成本效益的，并可能直接改进IBC患者的循证治疗指南。尽管这些统计方法是由国际生物伦理委员会的研究推动的，但对于应对任何慢性病，特别是罕见疾病的CER挑战，这些统计方法将是有用的。本项目开发的所有分析和统计工具软件一经验证，将向更广泛的研究界提供。

 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.