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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

基本信息

批准号：
10634549
负责人：
Xiaofei Wang
金额：
$ 38万
依托单位：
NORTH CAROLINA STATE UNIVERSITY RALEIGH
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-01 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10634549
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 Health Heterogeneity Knowledge Malignant Neoplasms Malignant neoplasm of esophagus Medical Research Medicare Methods Modality Modeling Non-Small-Cell Lung Carcinoma Operative Surgical Procedures Outcome Patient Recruitments Patient-Centered Care Patients Pattern Population Randomized Registries Research Research Personnel Resected SEER Program Sample Size Sampling Sampling Studies Source Statistical Data Interpretation Statistical Methods Target Populations Therapy trial Treatment Efficacy Work anticancer research cancer therapy chemoradiation clinical practice clinical trial analysis cohort data fusion data integration disease registry empowerment esophageal cancer patient improved inclusion criteria individualized medicine machine learning method multiple data sources novel older patient patient population population based precision medicine randomized, clinical trials safety study treatment and outcome treatment comparison treatment effect treatment group treatment response treatment strategy tumor

项目摘要

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.

项目概要/摘要随机临床试验（RCT）是评估癌症治疗的金标准方法，全世界巨大的健康和经济负担。然而，实际考虑因素允许 RCT 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，以便研究没有足够的能力将治疗效果推广到老年患者或其他代表性不足的患者的规定。另一方面，海量现实世界数据（RWD）越来越多地被基于人口的数据所捕获。数据库和登记处，例如监测、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和临床多样性队列。使用因果推理方法和 RWD 进行治疗评估，这些方法并非纯粹为了重新收集而收集现在经常执行搜索目的，但充满了局限性，例如由于缺乏随机化。事实上，在匹配分析中，RCT 和 RWD 结果之间的一致性通常很低。 RCT和RWD研究具有相同的治疗比较。尽管一些国家组织和监管机构监管机构提倡使用 RWD 来补充随机对照试验，这些方法将这两种方法潜在地结合在一起补充数据源并比单独使用单一数据源实现更好的治疗评估尚待开发。该提案是由 PI 共同研究治疗策略的安全性和有效性而提出的通过整合多个方面的数据，针对老年非小细胞肺癌（NSCLC）和食管癌患者来源：NCI 合作小组的随机对照试验和真实世界数据库（例如 SEER、SEER-Medicare 和国家数据库）。该项目的目标是开发新的统计方法，用于随机对照试验和随机对照试验的综合分析 RWD 可以提高 RCT 结果的普遍性和估计效率，使其更加多样化 “现实世界”的患者以及未充分研究的人群，同时避免 RWD 固有的混杂偏差。在目标 1，我们开发 RCT 数据统计分析方法，以比较放化疗模式通过利用 SEER 中可比患者的基线协变量来分析现实世界和老年 NSCLC 患者，对于他们来说，化疗和放疗的时间信息以及结果都缺失。目标2 3 重点关注 RCT 和 RWD 提供可比较的协变量、治疗和结果时的设置信息。在目标 2 中，我们改进了 RCT 数据分析，以评估三联疗法与手术的比较通过利用大样本量和预测性，单独针对现实世界和老年食管癌患者由 NCDB/SEER-Medicare 提供权力。在目标 3 中，我们开发了新的高效且数据自适应的方法评估辅助化疗与观察的个体化治疗效果，可能会根据年龄进行修改通过整合 RCT 和 NCDB 数据，针对 IB 期切除的 NSCLC 患者确定肿瘤大小和肿瘤大小。