权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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.

项目总结/文摘