Conference Scene: The healthcare reform act, comparative effectiveness research and personalized medicine

2011 ◽  
Vol 8 (2) ◽  
pp. 133-135 ◽  
Author(s):  
Elizabeth Ofili ◽  
Dean Sproles
Keyword(s):  
Personalized Medicine ◽  
Comparative Effectiveness Research ◽  
Comparative Effectiveness ◽  
Healthcare Reform ◽  
Effectiveness Research ◽  
And Personalized Medicine

scholarly journals Comparative Effectiveness Research, Genomics-Enabled Personalized Medicine, and Rapid Learning Health Care: A Common Bond

2012 ◽  
Vol 30 (34) ◽  
pp. 4233-4242 ◽  
Author(s):  
Geoffrey S. Ginsburg ◽  
Nicole M. Kuderer
Keyword(s):  
Health Care ◽  
Personalized Medicine ◽  
Comparative Effectiveness Research ◽  
Comparative Effectiveness ◽  
Clinical Utility ◽  
Tumor Biology ◽  
Effectiveness Research ◽  
Rapid Learning ◽  
And Personalized Medicine ◽  
Evidence Generation

Despite stunning advances in our understanding of the genetics and the molecular basis for cancer, many patients with cancer are not yet receiving therapy tailored specifically to their tumor biology. The translation of these advances into clinical practice has been hindered, in part, by the lack of evidence for biomarkers supporting the personalized medicine approach. Most stakeholders agree that the translation of biomarkers into clinical care requires evidence of clinical utility. The highest level of evidence comes from randomized controlled clinical trials (RCTs). However, in many instances, there may be no RCTs that are feasible for assessing the clinical utility of potentially valuable genomic biomarkers. In the absence of RCTs, evidence generation will require well-designed cohort studies for comparative effectiveness research (CER) that link detailed clinical information to tumor biology and genomic data. CER also uses systematic reviews, evidence-quality appraisal, and health outcomes research to provide a methodologic framework for assessing biologic patient subgroups. Rapid learning health care (RLHC) is a model in which diverse data are made available, ideally in a robust and real-time fashion, potentially facilitating CER and personalized medicine. Nonetheless, to realize the full potential of personalized care using RLHC requires advances in CER and biostatistics methodology and the development of interoperable informatics systems, which has been recognized by the National Cancer Institute's program for CER and personalized medicine. The integration of CER methodology and genomics linked to RLHC should enhance, expedite, and expand the evidence generation required for fully realizing personalized cancer care.

Personalized Medicine in the Context of Comparative Effectiveness Research

2013 ◽  
Vol 16 (2) ◽  
pp. S73-S86 ◽  
Author(s):  
Anirban Basu
Keyword(s):  
Decision Making ◽  
Personalized Medicine ◽  
Comparative Effectiveness Research ◽  
Comparative Effectiveness ◽  
Genomic Research ◽  
Future Research ◽  
Effectiveness Research ◽  
Medical Quality ◽  
Clinical Scenarios ◽  
Objective Evidence

Abstract The world of patient-centered outcomes research (PCOR) seems to bridge the previously disjointed worlds of comparative effectiveness research (CER) and personalized medicine (PM). Indeed, theoretical reasoning on how information on medical quality should inform decision making, both at the individual and the policy level, reveals that personalized information on the value of medical products is critical for improving decision making at all levels. However, challenges to generating, evaluating and translating evidence that might lead to personalization need to be critically assessed. In this paper, I discuss two different concepts of personalized medicine – passive personalization (PPM) and active personalization (APM) that are important to distinguish in order to invest efficiently in PCOR and develop objective evidence on the value of personalization that will aid in its translation. APM constitutes the process of actively seeking identifiers, which can be genotypical, phenotypical or even environmental, that can be used to differentiate between the marginal benefits of treatment across patients. In contrast, PPM involves a passive approach to personalization where, in the absence of explicit research to discover identifiers, patients and physicians “learn by doing” mostly due to the repeated use of similar products on similar patients. Benchmarking the current state of PPM sets the bar to which the expected value of any new APM agenda should be evaluated. Exploring processes that enable PPM in practice can help discover new APM agendas, such as those based on developing predictive algorithms based on clinical, phenotypical and preference data, which may be more efficient that trying to develop expensive genetic tests. It can also identify scenarios or subgroups of patients where genomic research would be most valuable since alternative prediction algorithms were difficult to develop in those settings. Two clinical scenarios are discussed where PPM was explored through novel econometric methods. Related discussions around exploring PPM processes, multi-dimensionality of outcomes, and a balanced agenda for future research on personalization follow.

scholarly journals How Comparative Effectiveness Research Can Help Advance ‘Personalized Medicine’ In Cancer Treatment

2011 ◽  
Vol 30 (12) ◽  
pp. 2259-2268 ◽  
Author(s):  
Scott D. Ramsey ◽  
David Veenstra ◽  
Sean R. Tunis ◽  
Louis Garrison ◽  
John J. Crowley ◽  
...  
Keyword(s):  
Personalized Medicine ◽  
Cancer Treatment ◽  
Comparative Effectiveness Research ◽  
Comparative Effectiveness ◽  
Effectiveness Research
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