Inclusion of Minority Patients in Breast Cancer Clinical Trials: The Role of the Clinical Trial Environment

2009 ◽  
Author(s):  
Celia P. Kaplan
Keyword(s):  
Breast Cancer ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Cancer Clinical Trials

Identification of Cancer Care and Protocol Characteristics Associated With Recruitment in Breast Cancer Clinical Trials

2008 ◽  
Vol 26 (27) ◽  
pp. 4458-4465 ◽  
Author(s):  
Julie Lemieux ◽  
Pamela J. Goodwin ◽  
Kathleen I. Pritchard ◽  
Karen A. Gelmon ◽  
Louise J. Bordeleau ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Cancer Care ◽  
Primary Objective ◽  
Cancer Clinical Trials ◽  
Cooperative Groups ◽  
Number Of Patients ◽  
Trial Protocols ◽  
Few Data

Purpose It is estimated that only 5% of patients with cancer participate in a clinical trial. Barriers to participation may relate to available protocols, physicians, and patients, but few data exist on barriers related to cancer care environments and protocol characteristics. Methods The primary objective was to identify characteristics of cancer care environments and clinical trial protocols associated with a low recruitment into breast cancer clinical trials. Secondary objectives were to determine yearly recruitment fraction onto clinical trials from 1997 to 2002 in Ontario, Canada, and to compare recruitment fraction among years. Questionnaires were sent to hospitals requesting characteristics of cancer care environments and to cooperative groups/pharmaceutical companies for information on protocols and the number of patients recruited per hospital/year. Poisson regression was used to estimate the recruitment fraction. Results Questionnaire completion rate varied between 69% and 100%. Recruitment fraction varied between 5.4% and 8.5% according to year. More than 30% of patients were diagnosed in hospitals with no available trials. In multivariate analysis, the following characteristics were associated with recruitment: use of placebo versus not (relative risk [RR] = 0.80; P = .05), nonmetastatic versus metastatic trial (RR = 2.80; P < .01), and for nonmetastatic trials, protocol allowing an interval of 12 weeks or longer versus less than 12 weeks (from diagnosis, surgery, or end of therapy) before enrollment (RR = 1.36; P < .01). Conclusion Allowable interval of 12 weeks or longer to randomly assign patients in clinical trials could help recruitment. In our study, absence of an available clinical trial represented the largest barrier to recruitment.

The role of biologic therapy in phase II breast cancer clinical trials.

2011 ◽  
Vol 29 (15_suppl) ◽  
pp. e13048-e13048
Author(s):  
K. Patel ◽  
A. Kamal ◽  
T. Zhang ◽  
A. Schneider ◽  
E. P. Hamilton ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Clinical Trials ◽  
Phase Ii ◽  
Biologic Therapy ◽  
Cancer Clinical Trials

Proposal for Standardized Definitions for Efficacy End Points in Adjuvant Breast Cancer Trials: The STEEP System

2007 ◽  
Vol 25 (15) ◽  
pp. 2127-2132 ◽  
Author(s):  
Clifford A. Hudis ◽  
William E. Barlow ◽  
Joseph P. Costantino ◽  
Robert J. Gray ◽  
Kathleen I. Pritchard ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Cancer Clinical Trial ◽  
Cancer Clinical Trials ◽  
Cancer Death ◽  
End Points ◽  
Breast Cancer Death ◽  
End Point ◽  
Adjuvant Breast Cancer

Purpose Standardized definitions of breast cancer clinical trial end points must be adopted to permit the consistent interpretation and analysis of breast cancer clinical trials and to facilitate cross-trial comparisons and meta-analyses. Standardizing terms will allow for uniformity in data collection across studies, which will optimize clinical trial utility and efficiency. A given end point term (eg, overall survival) used in a breast cancer trial should always encompass the same set of events (eg, death attributable to breast cancer, death attributable to cause other than breast cancer, death from unknown cause), and, in turn, each event within that end point should be commonly defined across end points and studies. Methods A panel of experts in breast cancer clinical trials representing medical oncology, biostatistics, and correlative science convened to formulate standard definitions and address the confusion that nonstandard definitions of widely used end point terms for a breast cancer clinical trial can generate. We propose standard definitions for efficacy end points and events in early-stage adjuvant breast cancer clinical trials. In some cases, it is expected that the standard end points may not address a specific trial question, so that modified or customized end points would need to be prospectively defined and consistently used. Conclusion The use of the proposed common end point definitions will facilitate interpretation of trial outcomes. This approach may be adopted to develop standard outcome definitions for use in trials involving other cancer sites.

scholarly journals Adherence to the National Quality Forum (NQF) Breast Cancer Measures Within Cancer Clinical Trials: A Review From ACOSOG Z0010

2010 ◽  
Vol 17 (8) ◽  
pp. 1989-1994 ◽  
Author(s):  
Lee G. Wilke ◽  
Karla V. Ballman ◽  
Linda M. McCall ◽  
Armando E. Giuliano ◽  
Pat W. Whitworth ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Clinical Trials ◽  
Cancer Clinical Trials ◽  
National Quality Forum ◽  
National Quality

Practical Aspects of Implementing and Applying Health Care Cloud Computing Services and Informatics to Cancer Clinical Trial Data

2021 ◽  
pp. 826-832
Author(s):  
Jay G. Ronquillo ◽  
William T. Lester
Keyword(s):  
Health Care ◽  
Clinical Trial ◽  
Cancer Research ◽  
Cloud Computing ◽  
Clinical Trials ◽  
Laboratory Tests ◽  
Cancer Clinical Trial ◽  
Cancer Clinical Trials ◽  
Precision Oncology ◽  
Oncology Research

PURPOSE Cloud computing has led to dramatic growth in the volume, variety, and velocity of cancer data. However, cloud platforms and services present new challenges for cancer research, particularly in understanding the practical tradeoffs between cloud performance, cost, and complexity. The goal of this study was to describe the practical challenges when using a cloud-based service to improve the cancer clinical trial matching process. METHODS We collected information for all interventional cancer clinical trials from ClinicalTrials.gov and used the Google Cloud Healthcare Natural Language Application Programming Interface (API) to analyze clinical trial Title and Eligibility Criteria text. An informatics pipeline leveraging interoperability standards summarized the distribution of cancer clinical trials, genes, laboratory tests, and medications extracted from cloud-based entity analysis. RESULTS There were a total of 38,851 cancer-related clinical trials found in this study, with the distribution of cancer categories extracted from Title text significantly different than in ClinicalTrials.gov ( P < .001). Cloud-based entity analysis of clinical trial criteria identified a total of 949 genes, 1,782 laboratory tests, 2,086 medications, and 4,902 National Cancer Institute Thesaurus terms, with estimated detection accuracies ranging from 12.8% to 89.9%. A total of 77,702 API calls processed an estimated 167,179 text records, which took a total of 1,979 processing-minutes (33.0 processing-hours), or approximately 1.5 seconds per API call. CONCLUSION Current general-purpose cloud health care tools—like the Google service in this study—should not be used for automated clinical trial matching unless they can perform effective extraction and classification of the clinical, genetic, and medication concepts central to precision oncology research. A strong understanding of the practical aspects of cloud computing will help researchers effectively navigate the vast data ecosystems in cancer research.

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