SOLICITATION NOTICE
R -- Treatment Management of Rare Cancers: Understanding and Evaluating Prediction Tools
- Notice Date
- 8/7/2013
- Notice Type
- Presolicitation
- NAICS
- 541990
— All Other Professional, Scientific, and Technical Services
- Contracting Office
- Department of Health and Human Services, National Institutes of Health, National Cancer Institute, Office of Acquisitions, 9609 Medical Center Drive, Room 1E128, Rockville, Maryland, 20852, United States
- ZIP Code
- 20852
- Solicitation Number
- NCI-130112-TG
- Archive Date
- 9/5/2013
- Point of Contact
- Terry Galloway, Phone: 240-276-5384, Seena Ninan, Phone: 240-276-5419
- E-Mail Address
-
gallowaytl@mail.nih.gov, ninans@mail.nih.gov
(gallowaytl@mail.nih.gov, ninans@mail.nih.gov)
- Small Business Set-Aside
- N/A
- Description
- Contracting Office Address Department of Health and Human Services, National Institutes of Health, National Cancer Institute, Office of Acquisitions, 9609 Medical Center Drive, Room 1E136, Bethesda, MD 20892, UNITED STATES Description The National Cancer Institute (NCI), Division of Cancer Control and Population Sciences (DCCPS), Clinical and Translational Epidemiology Branch (CTEB) plans to procure on a sole source basis services to provide understanding and evaluating for prediction tools for rare cancer treatment management from Brown University, 1 Prospect Street, Providence, RI 02912-9079 This acquisition will be processed in accordance with simplified acquisition procedures as stated in FAR Part 13.106-1(b)(1). The North American Industry Classification System code is 541990 and the business size standard is $14.0 million. Only one award will be made as a result of this solicitation. This will be awarded as a firm fixed price type contract. Period of Performance shall be from award through twelve (12) months. It has been determined there are no opportunities to acquire green products or services for this procurement. One of the primary functions of the CTEB is to plan, develop, direct, coordinate, and evaluate a program of epidemiologic research to improve health outcomes by developing and disseminating evidence-based information to patients, clinicians, and other stakeholders, about which interventions are most effective for which patients under specific circumstances. To optimize patient outcomes, oncology healthcare providers require accurate tools which can differentiate between cancer patients that will respond to certain therapies and patients that are at highest risk of cancer progression or recurrence, a new primary cancer, or an adverse event. The decision-making process for cancer treatment management is complex and challenging because it requires balancing the clinical benefit of a treatment regimen with life expectancy, patient values, treatment goals, comorbidities, and potential treatment-related adverse events. Benefit/risk predictive tools are statistical models developed by combining and synthesizing data from clinical trials and observational studies to predict probabilities of outcome(s). Prediction tools have repeatedly been shown to be more accurate at predicting risk than healthcare providers alone and greatly aid and improve informed decision-making on treatment and post-treatment strategies. There is a significant need to develop and validate cancer site-specific benefit/risk statistical prediction tools that will provide oncologists and their patients with accurate estimates of treatment success with different regimens, as well as estimates of their potential complications and long-term morbidity. Although there are a number of prognostic risk assessment models for common cancers, prediction rules and benefit-risk prediction tools for rare cancer treatment management have not been comprehensively evaluated for their use and usefulness in clinical practice. The goal of this project is to (1) conduct a comprehensive review that would identify, synthesize, and appraise the scientific literature on the analytic validity, clinical validity and clinical utility of peer-reviewed benefit and risk prediction tools for cancer treatment management, (2) determine their utilization of these tools in oncology practice, (3) develop a searchable database that provides a comprehensive, unbiased, centralized, publicly available collection of prediction tools for cancer treatment management, and provide recommendations on what type of future tools are needed by oncologists and their patients to better predict treatment effects. Data sources may include MEDLINE, Cochrane CENTRAL, Cochrane Database of Systematic Reviews, EMBASE, and professional society guidelines (e.g. NCCN and ASCO). This project would inform the development of a future NCI-sponsored workshop and identify future research initiatives related to benefit-risk prediction models for cancer treatment management. The tasks for this specific project are (1) Conduct a horizon scan of the published literature, including clinical practice guidelines, on predictive instruments for selected rarer cancers (2) Conduct a horizon scan of the published literature, including clinical practice guidelines, on predictive instruments for selected rarer cancers, (3) Identify published information on the uptake or use of selected models in the United States. This project requires expertise in molecular epidemiology, genomics, clinical oncology, risk prediction, evidence-based medicine, systematic reviews, and developing online databases and resources. Brown University and their Center for Evidence-based Medicine are uniquely qualified to complete this work with the highest quality and in a short timeline. Brown University has extensive experience in conducting systematic review and horizon scans for studies related specifically to the development, evaluation and implementation of risk prediction models and benefit-risk tools for cancer treatment management. Specifically, they have developed proprietary statistical methodology evaluation criteria and specific literature searching methods and code, all for the examination and interpretation of the medical research related to not just prognostic risk prediction markers of cancer treatment management but also for predictive models. NCI is not aware of any other University or institute that has already developed specific methodological methods and study criteria for searching the literature and developing a database focused only on the topic benefit risk models for cancer treatment management. In addition, Brown University scientists developed the GeneTestTrackr database; software is used at the Center to track information on new genetic tests, which is then used to generate reports on their characteristics and also is required to allow for comprehensive searching of the literature to develop a database specific to predictive and prognostic benefit-risk models for cancer treatment management. Brown University's team has extensive knowledge and experience in conducting systematic reviews, as well as experience developing databases, makes it uniquely qualified to facilitate this work. This notice is not a request for competitive quotation. However, if any interested party believes it can meet the above requirement, it may submit a statement of capabilities. The statement of capabilities and any other information furnished must be in writing and must contain material in sufficient detail to allow NCI to determine if the party can perform the requirement. Capability statements must be received in the contracting office by 11:00 AM EST, on August 21, 2013. All responses and questions must be in writing and faxed 240-276-5399 or emailed to Terry Galloway, Contracting Officer via electronic mail at gallowaytl@mail.nih.gov. A determination by the Government not to compete this proposed requirement based upon responses to this notice is solely within the discretion of the Government. Information received will be considered solely for the purpose of determining whether to conduct a competitive procurement. No collect calls will be accepted. In order to receive an award, contractors must be registered and have valid certification in the Central Contractor Registration (CCR) and the Online Representations and Certifications Applications (ORCA) through sam.gov. Reference: NCI-130112-TG on all correspondence.
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