Grant Number:	5R21CA235154-02 Interpret this number
Primary Investigator:	Lipitz Snyderman, Allison
Organization:	Sloan-Kettering Inst Can Research
Project Title:	Linking Population-Based Data Sources to Examine Health Disparities in Clinical Trial Participation and Outcomes
Fiscal Year:	2020

PROJECT SUMMARY We propose a feasibility study to support the creation of a population-based data resource that has the potential for detailed, insightful studies about the practice and impact of cancer clinical trials. In 2000, the U.S. National Library of Medicine resource ClinicalTrials.gov was made publicly available. This resource contains details of clinical trials sponsored by the NIH, other Federal agencies, and non-profit and private organizations, including cancer clinical trials. In September 2008, Medicare claims were modified to include an identifier for the clinical trial to which a patient is enrolled, the National Clinical Trial (NCT) identifier. This is the same identifier used by ClinicalTrials.gov. In 2014, reporting of the NCT identifier became mandatory for all claims submitted to Medicare for clinical trial related services. Thus, from 2014 onwards, it is theoretically possible to identify nearly all Fee-for-Service (FFS) Medicare patients who are registered on a cancer clinical trial. It should also be possible to link these patients with detailed clinical trial information from ClinicalTrials.gov using the NCT identifier. These Medicare claims have been linked to other data sources such as the Surveillance, Epidemiology, and End Results Program cancer registry and databases with information on hospitals and physicians. Therefore, it should be possible to create a resource that tracks Medicare participants on clinical trials for outcomes such as survival, hospitalizations, and costs. It would also link individual patients and their treating physicians and hospitals with details of the clinical trial. If this resource proves as successful as hypothesized, it would have immense opportunity for providing population-based information about clinical trials in the U.S., specifically covering questions related to disparities. However, its potential value extends far beyond the study of cancer disparities. The kinds of population-based questions that could be addressed include: What are the institutional and provider characteristics associated with trial enrollment overall and among underrepresented groups? Is the lower enrollment of black and Hispanic patients on cancer trials due to these patients being treated at institutions with limited or no availability of trials? What is the distance from participant home to the nearest available trial site and how does this differ by patient characteristics? These and other pressing research questions are only possible to address if the record linkage is successful and the data quality is adequate. Thus, the purpose of this proposal is to assess the feasibility of creating this linked data resource. In Aim 1, we will create the record linkages and assemble details about the trials, patients, providers, and outcomes. In Aim 2, we will examine carefully the validity of the crucial linkages that define the proposed new resource. In Aim 3, we will generate data to address three exemplar questions regarding disparities, and map out a more exhaustive set of feasible research questions that would shed light on important aspects of the U.S. clinical trials enterprise.




Clinical Trial Participation Among Older Adult Medicare Fee-for-Service Beneficiaries With Cancer.
Authors: Green A.K. , Tabatabai S.M. , Aghajanian C. , Landgren O. , Riely G.J. , Sabbatini P. , Bach P.B. , Begg C.B. , Lipitz-Snyderman A. , Mailankody S. .
Source: JAMA oncology, 2022-12-01; 8(12), p. 1786-1792.
PMID: 36301585
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Validation of a Population-Based Data Source to Examine National Cancer Clinical Trial Participation.
Authors: Green A.K. , Tabatabai S.M. , Bai X. , Mishra Meza A. , Lesny A.M. , Aghajanian C. , Landgren O. , Riely G.J. , Sabbatini P. , Salner A. , et al. .
Source: JAMA network open, 2022-03-01; 5(3), p. e223687.
EPub date: 2022-03-01.
PMID: 35315914
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