Empower treatment effects evaluation of randomized clinical trials for elderly patients with integrated real-world data

项目总结/摘要 随机临床试验（RCT）是评估癌症治疗的金标准方法， 在全世界造成巨大的健康和经济负担。然而，允许RCT被 进行的研究通常需要相对较小的样本量和有限的资格标准， 不足以将治疗效果推广到老年患者或其他代表性不足的患者人群， ulations。另一方面，大量的真实世界数据（RWD）越来越多地被基于人口的 数据库和登记处，如监测、流行病学和最终结果（SEER）、SEER-Medicare，以及 国家癌症数据库（NCDB），与RCT相比，具有更广泛的人口统计学和临床多样性 同伙使用因果推断方法和RWD进行治疗评价，收集这些数据并非纯粹用于重新评估。 搜索目的现在被频繁地执行，但是充满了限制，诸如由于缺乏 随机化。事实上，RCT和RWD结果之间的一致性在匹配的分析中通常较低。 具有相同治疗比较的RCT和RWD研究。虽然有几个国家和地区组织， 监管机构提倡使用RWD来补充RCT，这种方法可以将这两种方法结合起来， 互补的数据源，并实现比单独使用单一数据源更好的治疗评价 还有待开发。 这项建议的动机是由PI合作研究治疗策略的安全性和有效性 老年非小细胞肺癌（NSCLC）和食管癌患者，通过整合来自多个 资料来源：来自NCI合作组和真实世界数据库（如SEER、SEER-Medicare和 NCDB）。本项目的目标是开发新的统计方法，用于RCT的综合分析， RWD可以提高RCT结果的普遍性并提高估计效率，以实现更多样化 “真实世界”患者以及研究不足的人群，同时避免RWD固有的混杂偏倚。在 目的1，我们开发RCT数据的统计分析方法，以比较 通过利用SEER中可比患者的基线协变量， 化疗和放疗的时间信息以及结果均缺失。目标2 和3关注RCT和RWD提供可比协变量、治疗和结局时的设置 信息.在目标2中，我们改进了随机对照试验数据的分析，以评价三联疗法与外科手术的比较 通过利用大样本量和预测性， NCDB/SEER-Medicare提供的电源。在目标3中，我们开发了新的有效和数据自适应的方法， 估计辅助化疗与观察的个体化治疗效果，可能根据年龄进行修改艾德 和肿瘤大小，对于IB期切除的NSCLC患者，通过整合RCT和NCDB数据。

PROJECT SUMMARY/ABSTRACT Randomized clinical trials (RCTs) are the gold-standard method of evaluating cancer treatment, which has immense health and economic burdens worldwide. However, practical considerations that allow an RCT to be conducted typically require a relatively small sample size and restricted eligibility criteria such that the study has inadequate power to generalize treatment effects to elderly patients or other under-represented patient pop- ulations. On the other hand, massive real-world data (RWD) are increasingly captured by population-based databases and registries, such as Surveillance, Epidemiology, and End Results (SEER), SEER-Medicare, and National Cancer Database (NCDB), that have much broader demographic and clinical diversity compared to RCT cohorts. Treatment evaluation using causal inference methods and RWD that were not collected purely for re- search purposes is now frequently performed but fraught with limitations such as confounding due to lack of randomization. In fact, the agreement between RCT and RWD findings is often low in the analysis of matched RCT and RWD studies with the same treatment comparisons. Although several national organizations and reg- ulatory agencies have advocated using RWD to complement RCTs, methods that integrate these two potentially complementary data sources and achieve better treatment evaluation over the use of a single data source alone have yet to be developed. This proposal is motivated by the PIs' collaborative work to study the safety and efficacy of treatment strategies for elderly non-small cell lung cancer (NSCLC) and esophageal cancer patients by integrating data from multiple sources: RCTs from NCI cooperative groups and the real-world databases (e.g. SEER, SEER-Medicare, and NCDB). The objective of this project is to develop new statistical methods for integrative analyses of RCTs and RWD that can improve the generalizability and increase estimation efficiency of RCT findings to more diverse "real-world" patients as well as under-studied populations while avoiding confounding bias inherent in RWD. In Aim 1, we develop methods for statistical analysis of RCT data to compare chemoradiotherapy patterns for the real-world and elderly NSCLC patients by leveraging the baseline covariates of comparable patients from SEER, for whom the temporal information of chemotherapy and radiation and the outcome are both missing. Aims 2 and 3 focus on the settings when both RCT and RWD provide comparable covariates, treatment, and outcome information. In Aim 2, we develop improved analysis of RCT data to evaluate trimodality therapy versus surgery alone for the real-world and elderly esophageal cancer patients by exploiting the large sample size and predictive power offered by the NCDB/SEER-Medicare. In Aim 3, we develop new efficient and data-adaptive methods to estimate individualized treatment effects of adjuvant chemotherapy versus observation, possibly modified by age and tumor size, for stage IB resected NSCLC patients by integrating RCT and NCDB data.