Comparative Effectiveness of Cancer Research: Use Data from Multiple Sources

 描述（由申请人提供）：尽管肿瘤学的比较有效性研究（CER）引起了大量关注，以提供及时的治疗比较并改善健康结局，但在利用多种数据来源以及有效的统计方法来收集CER中的证据方面仍存在相当大的方法学差距。这项研究的直接动机是我们与乳腺癌医学肿瘤学家和外科医生合作调查炎性乳腺癌（IBC），这是一种罕见但侵袭性的乳腺癌。本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基于人群的登记数据库中有或没有抽样误差的汇总信息相结合，开发统计方法和风险预测模型。在这个项目中，（目的1）我们提出了统计方法，利用外部数据的汇总信息时，分析主要队列数据与个体患者水平的数据下的参数和半参数模型的生存数据，并提供一个测试程序来评估信息的可比性，从主要队列数据和外部数据。我们将进一步推广的方法，以占总信息的不确定性的估计和推断程序的生存数据（目标2）。此外，（目标3）我们将把具有详细风险特征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，以开发一种新的综合性IBC特异性死亡风险预测模型，并提供一种估计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，我们提出的方法，最大限度地利用现有的IBC队列数据，将它们与外部注册数据库相结合，是具有成本效益的，并可能直接改善循证治疗指南的IBC患者。虽然受到IBC研究的启发，但统计方法将有助于解决CER在任何慢性疾病，特别是罕见疾病中的挑战。在这个项目中开发的所有分析和统计工具软件一旦得到验证，将提供给更广泛的研究界。

 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.