Rahber Thariani

Cost Effectiveness of Pharmacogenomics

The use of pharmacogenetic testing in clinical practice is limited thus far. A potential barrier to the widespread implementation of pharmacogenetic testing is the lack of evidence on whether testing provides good value for money. The objective of this review was to provide a systematic and critical review of economic evaluations of pharmacogenetic testing. A literature search using publically available databases was performed for articles published up to October 2009. To be included, studies had to meet the definition of being a pharmacogenomic study (defined as use of information on human genetic variation to target drug therapy) and an economic evaluation (defined as an evaluation of both costs and clinical outcomes). Articles that met these criteria were subsequently reviewed and graded using the Quality of Health Economic Studies (QHES) instrument. Lastly, the evidence for biomarker validity and utility were qualitatively assessed using expert opinion.A total of 34 articles were identified using our defined criteria. The most common disease category was thromboembolic-related diseases (26%), while the most common biomarkers were thiopurine methyltransferase and cytochrome P450 2C9 (18% each). Almost all studies were published after 2004 (91%). Two types of studies were identified: cost-effectiveness studies and cost-utility studies, with roughly half of the overall studies being cost-utility studies (53%) and a majority of these published within the last 3 years. The average quality score was 77 (range 29–99). Of the biomarkers reviewed, it was estimated that most had demonstrated clinical validity, but only two had demonstrated clinical utility.Despite a recent increase in the number of economic evaluations of pharmacogenetic applications, further studies examining the clinical validity and utility of these biomarkers are needed to support cost-effectiveness assessments.

Value-of-information analysis within a stakeholder-driven research prioritization process in a US setting: an application in cancer genomics.

Objective. The objective of this study was to evaluate the feasibility and outcomes of incorporating value-of-information (VOI) analysis into a stakeholder-driven research prioritization process in a US-based setting. Methods. Within a program to prioritize comparative effectiveness research areas in cancer genomics, over a period of 7 months, we developed decision-analytic models and calculated upper-bound VOI estimates for 3 previously selected genomic tests. Thirteen stakeholders representing patient advocates, payers, test developers, regulators, policy makers, and community-based oncologists ranked the tests before and after receiving VOI results. The stakeholders were surveyed about the usefulness and impact of the VOI findings. Results. The estimated upper-bound VOI ranged from

Tumor Marker Usage and Medical Care Costs Among Older Early-Stage Breast Cancer Survivors

33 million to

Paying for personalized care: cancer biomarkers and comparative effectiveness.

2.8 billion for the 3 research areas. Seven stakeholders indicated the results modified their rankings, 9 stated VOI data were useful, and all indicated they would support its use in future prioritization processes. Some stakeholders indicated expected value of sampled information might be the preferred choice when evaluating specific study designs. Limitations. Our study was limited by the size and the potential for selection bias in the composition of the external stakeholder group, lack of a randomized design to assess effect of VOI data on rankings, and the use of expected value of perfect information v. expected value of sample information methods. Conclusions. Value of information analyses may have a meaningful role in research topic prioritization for comparative effectiveness research in the United States, particularly when large differences in VOI across topic areas are identified. Additional research is needed to facilitate the use of more complex value of information analyses in this setting.

Prioritization in Comparative Effectiveness Research: The CANCERGEN Experience in Cancer Genomics

PURPOSE Although American Society of Clinical Oncology guidelines discourage the use of tumor marker assessment for routine surveillance in nonmetastatic breast cancer, their use in practice is uncertain. Our objective was to determine use of tumor marker tests such as carcinoembryonic antigen and CA 15-3/CA 27.29 and associated Medicare costs in early-stage breast cancer survivors. METHODS By using Surveillance, Epidemiology, and End Results-Medicare records for patients diagnosed with early-stage breast cancer between 2001 and 2007, tumor marker usage within 2 years after diagnosis was identified by billing codes. Logistic regression models were used to identify clinical and demographic factors associated with use of tumor markers. To determine impact on costs of care, we used multivariable regression, controlling for other factors known to influence total medical costs. RESULTS We identified 39,650 eligible patients. Of these, 16,653 (42%) received at least one tumor marker assessment, averaging 5.7 tests over 2 years, with rates of use per person increasing over time. Factors significantly associated with use included age at diagnosis, diagnosis year, stage at diagnosis, race/ethnicity, geographic region, and urban/rural status. Rates of advanced imaging, but not biopsies, were significantly higher in the assessment group. Medical costs for patients who received at least one test were approximately 29% greater than costs for those who did not, adjusting for other factors. CONCLUSION Breast cancer tumor markers are frequently used among women with early-stage disease and are associated with an increase in both diagnostic procedures and total cost of care. A better understanding of factors driving the use of and the potential benefits and harms of surveillance-based tumor marker testing is needed.

Prioritization in comparative effectiveness research: the CANCERGEN Experience.

Genomic‐based diagnostics can play a key role in creating a more efficient healthcare system by directing patients toward beneficial therapies and away from therapies that pose substantial risk or are unlikely to improve outcomes for the patient. We outline how the value provided by diagnostics is closely linked to a range of factors including magnitude of health outcome improvement, avoiding adverse effect, diagnostic parameters, process of care, resource utilization, and costs. Comparative effectiveness approaches to evidence generation, including health outcome measurements, quality of life, economic analyses, decision modeling, and pragmatic clinical trials, can be used to provide stakeholders with a range of information to inform treatment, guidelines, coverage, and reimbursement decisions. Evidence of comparative effectiveness can also help support value‐based reimbursement of cancer biomarkers and treatment strategies as means of paying for personalized medicine.

One-dimensional surface plasmon resonance imaging system using wavelength interrogation

Background:Systematic approaches to stakeholder-informed research prioritization are a central focus of comparative effectiveness research. Genomic testing in cancer is an ideal area to refine such approaches given rapid innovation and potentially significant impacts on patient outcomes. Objective:To develop and pilot test a stakeholder-informed approach to prioritizing genomic tests for future study in collaboration with the cancer clinical trials consortium SWOG. Methods:We conducted a landscape analysis to identify genomic tests in oncology using a systematic search of published and unpublished studies, and expert consultation. Clinically valid tests suitable for evaluation in a comparative study were presented to an external stakeholder group. Domains to guide the prioritization process were identified with stakeholder input, and stakeholders ranked tests using multiple voting rounds. Results:A stakeholder group was created including representatives from patient-advocacy groups, payers, test developers, regulators, policy makers, and community-based oncologists. We identified 9 domains for research prioritization with stakeholder feedback: population impact; current standard of care, strength of association; potential clinical benefits, potential clinical harms, economic impacts, evidence of need, trial feasibility, and market factors. The landscape analysis identified 635 studies; of 9 tests deemed to have sufficient clinical validity, 6 were presented to stakeholders. Two tests in lung cancer (ERCC1 and EGFR) and 1 test in breast cancer (CEA/CA15-3/CA27.29) were identified as top research priorities. Conclusions:Use of a diverse stakeholder group to inform research prioritization is feasible in a pragmatic and timely manner. Additional research is needed to optimize search strategies, stakeholder group composition, and integration with existing prioritization mechanisms.

Is a comparative clinical trial for breast cancer tumor markers to monitor disease recurrence warranted? A value of information analysis.

Background:Systematic approaches to stakeholder-informed research prioritization are a central focus of comparative effectiveness research. Genomic testing in cancer is an ideal area to refine such approaches given rapid innovation and potentially significant impacts on patient outcomes. Objective:To develop and pilot test a stakeholder-informed approach to prioritizing genomic tests for future study in collaboration with the cancer clinical trials consortium SWOG. Methods:We conducted a landscape analysis to identify genomic tests in oncology using a systematic search of published and unpublished studies, and expert consultation. Clinically valid tests suitable for evaluation in a comparative study were presented to an external stakeholder group. Domains to guide the prioritization process were identified with stakeholder input, and stakeholders ranked tests using multiple voting rounds. Results:A stakeholder group was created including representatives from patient-advocacy groups, payers, test developers, regulators, policy makers, and community-based oncologists. We identified 9 domains for research prioritization with stakeholder feedback: population impact; current standard of care, strength of association; potential clinical benefits, potential clinical harms, economic impacts, evidence of need, trial feasibility, and market factors. The landscape analysis identified 635 studies; of 9 tests deemed to have sufficient clinical validity, 6 were presented to stakeholders. Two tests in lung cancer (ERCC1 and EGFR) and 1 test in breast cancer (CEA/CA15-3/CA27.29) were identified as top research priorities. Conclusions:Use of a diverse stakeholder group to inform research prioritization is feasible in a pragmatic and timely manner. Additional research is needed to optimize search strategies, stakeholder group composition, and integration with existing prioritization mechanisms.

Getting our priorities straight: a novel framework for stakeholder-informed prioritization of cancer genomics research.

We report on a one-dimensional surface plasmon resonance imaging system that can measure reflectivity over a range of incident wavelengths on multiple samples in parallel. The design and operation of the instrument are described and two methods of quantification are demonstrated.We report on a one-dimensional surface plasmon resonance imaging system that can measure reflectivity over a range of incident wavelengths on multiple samples in parallel. The design and operation of the instrument are described and two methods of quantification are demonstrated.

Imaging of surfaces by concurrent surface plasmon resonance and surface plasmon resonance-enhanced fluorescence.

BACKGROUND Breast cancer tumor markers are used by some clinicians to screen for disease recurrence risk. Since there is limited evidence of benefit, additional research may be warranted. AIM To assess the potential value of a randomized clinical trial of breast tumor marker testing in routine follow-up of high-risk, stage II-III breast cancer survivors. MATERIALS & METHODS We developed a decision-analytic model of tumor marker testing plus standard surveillance every 3-6 months for 5 years. The expected value of sample information was calculated using probabilistic simulations and was a function of: the probability of selecting the optimal monitoring strategy with current versus future information; the impact of choosing the nonoptimal strategy; and the size of the population affected. RESULTS The value of information for a randomized clinical trial involving 9000 women was US