Detecting racial disparities in cancer survival by integrating multiple high-dimensional observational studies

项目概要/摘要 尽管整体有所改善，但民族或种族差异仍在继续扩大，这表明 理解差异的研究设计存在缺陷。例如，与 2017 年美国人口普查，大多数观察性癌症研究发现过度代表白人 非裔美国人和亚洲人的代表性不足。如何利用这些研究来检测和 了解种族差异仍然具有挑战性。 该提案是由波士顿肺癌生存队列 (BLCSC) 发起的，该队列是癌症研究中心之一 全球最大的肺癌队列，由 1992 年以来登记的肺癌病例组成 在达纳法伯癌症研究所 (DFCI) 和马萨诸塞州总医院 (MGH)，以及 已扩展到 MD 安德森癌症中心 (MDACC) 和梅奥诊所。这个丰富的数据库 为研究癌症结果的种族差异以及 跨种族群体的协变量不平衡带来了挑战。我们还可以访问 国际肺癌和癌症联盟 (ILCCO)，一个成立于 2007 年的国际队列 2004年，数据结构与BLSCC类似。 利用这些癌症队列，我们开发出方法，其共同目标是有效 通过整合高维观察来确定癌症结果的种族差异 对多个种族群体的研究。 BLCSC 和 ILLCO 等丰富的数据集非常适合集成、 无混杂地检测癌症结果中的种族差异，并生成统计数据 研究结果可推广到现实且具有包容性的更大人群。

PROJECT SUMMARY/ABSTRACT Despite overall improvements, ethnic or racial disparities continue to increase, suggesting deficiencies in research designs for understanding disparities. For example, compared to the 2017 US Census, most observational cancer studies were found to over represent Caucasians and underrepresent African Americans and Asians. How to utilize these studies to detect and understand racial disparities remains challenging. This proposal is motivated by the Boston Lung Cancer Survival Cohort (BLCSC), one of the largest lung cancer cohorts globally, which consists of lung cancer cases registered since 1992 at the Dana-Farber Cancer Institute (DFCI) and the Massachusetts General Hospital (MGH), and has expanded to the MD Anderson Cancer Center (MDACC) and Mayo Clinic. This rich database provides a unique opportunity for studying racial disparities in cancer outcomes as well as presents a challenge with unbalanced covariates across racial groups. We also have access to the International Lung and Cancer Consortium (ILCCO), an international cohort established in 2004 with a data structure similar to BLCSC. Leveraging these cancer cohorts, we develop methods with a common goal of effectively identifying racial disparities in cancer outcomes by integrating high dimensional observational studies with multiple racial groups. Rich datasets like BLCSC and ILLCO are ideal for integrative, unconfounded detection of racial disparities in cancer outcomes, and for generating statistical findings generalizable to a realistic and inclusive larger population.