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， 全世界的巨大健康和经济伯伦斯。但是，实用的考虑因素允许RCT为 进行的通常需要相对较小的样本量和限制资格标准，以便该研究 对老年患者或其他代表性不足的患者prop的能力不足以概括治疗效应 Ulations。另一方面，大量现实世界数据（RWD）越来越多地被基于人群的 数据库和注册表，例如监视，流行病学和最终结果（SEER），SEER-MEDICARE和 与RCT相比，国家癌症数据库（NCDB）具有更广泛的人口和临床多样性 同伙。使用因果推断方法和RWD的治疗评估，这些方法纯粹是为了重新收集的 现在经常执行搜索目的，但由于缺乏混淆等限制 随机化。实际上，在匹配的分析中，RCT和RWD发现之间的一致性通常很低 RCT和RWD研究具有相同的治疗比较。虽然几个国家组织和统计 欺凌机构已提倡使用RWD完成RCT，这是将这两种综合的方法 完全数据源并获得更好的治疗评估，仅使用单个数据源 尚未开发。 这项建议是由PIS的合作工作激励的，以研究治疗策略的安全性和有效性 对于较早的非小细胞肺癌（NSCLC）和食管癌患者，通过整合来自多个的数据 资料来源：来自NCI合作组和现实世界数据库的RCT（例如Seer，Seer-Medicare和 NCDB）。该项目的目的是开发新的统计方法，以进行RCT的集成分析和 RWD可以提高RCT发现对更多潜水员的估计效率的普遍性并提高估计的效率 “现实世界”的患者以及研究不足的人群，同时避免了RWD中的混淆偏见。在 AIM 1，我们开发了RCT数据统计分析的方法，以比较化学放疗模式的模式 通过利用SEER的可比患者的基线协变量，现实世界和NSCLC患者的较早的患者 对于他们而言，暂时的化学疗法和放射线以及结果都缺少。目标2 当RCT和RWD提供可比的协变量，治疗和结果时，将重点放在设置上 信息。在AIM 2中，我们开发了对RCT数据的改进分析，以评估三通疗法与手术 通过利用较大的样本和预测性来单独使用现实世界和古老的食管癌患者 NCDB/SEER-MEDICARE提供的功率。在AIM 3中，我们开发了新的效率和数据自适应方法 可调化疗与观察的估计个性化治疗效果，可能按年龄进行修改 对于IB期，通过整合RCT和NCDB数据来切除NSCLC患者的肿瘤大小。