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

利用综合的真实世界数据，对老年患者的随机临床试验的治疗效果进行评估

• 批准号: 10402256
• 负责人: Xiaofei Wang
• 金额: $ 38.55万
• 依托单位: NORTH CAROLINA STATE UNIVERSITY RALEIGH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10402256
• 关键词: Address; Adjuvant Chemotherapy; Advocate; Age; Agreement; Area; Calibration; Cancer Patient; Characteristics; Chemotherapy and/or radiation; Clinical; Clinical Trials; Complement; Data; Data Analyses; Data Collection; Data Sources; Databases; Decision Making; Disease; Economic Burden; Elderly; Electronic Health Record; Eligibility Determination; Ensure; Equilibrium; Evaluation; Evidence Based Medicine; Exclusion Criteria; Gold; Heterogeneity; Knowledge; Malignant Neoplasms; Malignant neoplasm of esophagus; Medical Research; Medicare; Methods; Modeling; Non-Small-Cell Lung Carcinoma; Operative Surgical Procedures; Outcome; Patient Recruitments; Patient-Centered Care; Patients; Pattern; Population; Randomized; Randomized Clinical Trials; Registries; Research; Research Personnel; Resected; SEER Program; Sample Size; Sampling; Sampling Studies; Source; Statistical Data Interpretation; Statistical Methods; Target Populations; Therapy trial; Treatment Efficacy; Treatment outcome; Weight; Work; anticancer research; base; cancer therapy; chemoradiation; clinical practice; clinical trial analysis; cohort; disease registry; esophageal cancer patient; health economics; improved; inclusion criteria; individualized medicine; learning strategy; machine learning method; multiple data sources; novel; older patient; patient population; population based; precision medicine; safety study; study population; treatment comparison; treatment effect; treatment group; treatment response; treatment strategy; tumor

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.

项目摘要/摘要 随机临床试验(RCT)是评估癌症治疗的金标准方法，它有 世界范围内巨大的健康和经济负担。然而，允许RCT被 所进行的研究通常需要相对较小的样本量和受限的资格标准，从而使研究 没有足够的力量将治疗效果推广到老年患者或其他代表性不足的患者- 恭喜你。另一方面，海量的真实世界数据(RWD)越来越多地被基于人口的数据捕获 数据库和登记处，如监测、流行病学和最终结果(SEER)、SEER-Medicare和 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 一群人。使用因果推断方法和RWD进行治疗评估，这些评估不是纯粹为重新收集的 搜索目的现在经常执行，但充满了限制，如由于缺乏搜索而造成的混淆 随机化。事实上，RCT和RWDfi编码之间的一致性在匹配分析中往往很低 RCT和RWD研究采用相同的治疗比较。尽管有几个国家组织和法规-- 监管机构主张使用RWD来补充RCT，这种方法可能会将这两种方法整合在一起 补充数据源并实现比单独使用单一数据源更好的处理评估 都还有待开发。 这项建议是由PIS合作研究治疗策略的安全性和有效性fi所推动的。 针对老年非小细胞肺癌(NSCLC)和食道癌患者，整合来自 资料来源：来自NCI合作组织和真实世界数据库(例如SEER、SEER-Medicare和 NCDB)。这个项目的目标是开发新的统计方法，用于RCT和RCT的综合分析 RWD，可以提高泛化能力，提高fi估计效率，使fi编码更加多样化 “真实世界”的患者以及研究不足的人群，同时避免RWD固有的混淆偏见。在……里面 目的1，我们开发了随机对照试验数据的统计分析方法，以比较放化疗模式。 真实世界和老年NSCLC患者通过利用SEER中可比患者的基线协变量， 对于他们来说，化疗和放射的时间信息以及结果都是缺失的。AIMS 2 以及3当RCT和RWD都提供可比较的协变量、治疗和结果时，重点放在设置上 信息。在目标2中，我们发展了对随机对照试验数据的改进分析，以评估三联疗法与手术治疗的优劣。 单独为现实世界和老年食道癌患者开发大样本和预测性 由NCDB/SEER-Medicare提供的电力。在目标3中，我们开发了新的有效的fi和数据自适应方法来 评估辅助化疗的个体化治疗效果与观察比较，可能根据年龄而改变fi 结合RCT和NCDB数据，对IB期切除的NSCLC患者的肿瘤大小进行评估。