Investment In Comparative Effectiveness Research Research Articles

ASSESSMENT OF COMPARATIVE EFFECTIVENESS RESEARCH GAPS TO PROMOTE AGING IN PLACE: A STAKEHOLDER-DRIVEN APPROACH

A priority for many older adults is to remain in their homes and communities. This paper describes the Patient-Centered Outcomes Research Institute’s (PCORI) review of our investment in comparative effectiveness research (CER) that will advance the field of “aging in place.” To inform our systematic evaluation, we first engaged our multi-stakeholder advisory panel of patients/caregivers, clinicians, health systems leaders, and payers to develop and refine a conceptual framework for research focused on aging in place among older adults. Key themes from stakeholders were: aging in place interventions should be patient-centered and align with the patients’ needs and goals, studies should include individuals 55+ years who may be at risk for institutionalization, relevant interventions go beyond environmental modifications and include social support, healthcare (inclusive of palliative care), and personal care services. In addition, aging in place services should focus more on informal caregiver interventions and outcomes since informal caregivers play a major role in enabling older adults to age in place. We identified 14 PCORI-funded CER projects that will provide evidence on the most effective interventions to promote aging in place among older adults; the total investment is $113.4 million dollars. The portfolio is addressing decisional dilemmas faced by multiple stakeholders on a variety of topics including falls prevention, home-based palliative care, and community-based care models; however, several critical CER evidence gaps remain that need to be addressed in future funding investments and will be discussed during the presentation.

Impact of Postapproval Evidence Generation on the Biopharmaceutical Industry

PurposeMeeting marketplace demands for proving the value of new products requires more data than the industry has routinely produced. These data include evidence from comparative effectiveness research (CER), including randomized, controlled trials; pragmatic clinical trials; observational studies; and meta-analyses. MethodsWe designed and conducted a survey to examine the industry’s perceptions on new data requirements regarding CER evidence, the acceptability of postapproval study types, payer-specific issues related to CER, communication of data being generated postapproval, and methods used for facilitating postapproval evidence generation. FindingsCER is being used by payers for most types of postapproval decisions. Randomized, controlled trials were indicated as the most acceptable form of evidence. At the same time, there was support for the utility of other types of studies, such as pragmatic clinical trials and observational studies. Respondents indicated the use of multiple formats for communicating postapproval data with many different stakeholders including regulators, payers, providers, and patients. Risk-sharing agreements with payers were unanimously supported by respondents with regard to certain products with unclear clinical and economic outcomes at launch. In these instances, conditional reimbursement through coverage with evidence development was considered a constructive option. The Food and Drug Administration’s initiative called Regulatory Science was considered by the respondents as having the most impact on streamlining the generation of postapproval research-related evidence. ImplicationsThe biopharmaceutical industry is faced with a broad and complex set of challenges related to evidence generation for postapproval decisions by a variety of health care system stakeholders. Uncertainty remains as to how the industry and payers use postapproval studies to guide decision making with regard to pricing and reimbursement status. Correspondingly, there is uncertainty regarding whether the industry’s investment in CER will have a positive return on investment in terms of reimbursement and market access.

The American Reinvestment and Recovery Act investment in comparative effectiveness research: lessons learned and implications for future investments.

2014 The Journal of Comparative Effectiveness Research is pleased to bring you this special issue examining the lessons that have been learned following the American Reinvestment and Recovery Act (ARRA) investment in comparative effectiveness research (CER), guest edited by Sheldon Greenfield (U niversity of California, Irvine). The ARRA was signed into law in the USA in 2009, and involved economic stimulus in many areas, including healthcare. Specifically, the Act assigned US$1.1 billion to study the comparative effectiveness of h ealthcare treatments. This special issue of the Journal of Comparative Effectiveness Research presents initial findings from a midstream assessment conducted by Mathematica Policy Research. This unique portfolio of investments included more than 420 projects managed by major US government agencies and conducted by university researchers and private sector contract research firms. The articles included here describe the ARRA CER investments at a high level, provide detailed analysis of core areas of the investment, identify public perceptions of CER at the time of the investment, and includes lessons learned for future investments in CER. The issue also includes research conducted by AcademyHealth, focusing on ARRA CER infrastructure investments, including those to support learning health systems. The accomplishments achieved to date bode well for the future for CER; it is worth bearing in mind that many of the principles and methods being used in these projects were only just being developed at the time the ARRA funding was awarded. Indeed, some of these principles (including, for example, proper comparisons, heterogeneity of treatment effects, stakeholder engagement, assessment of harms and electronic data infrastructure, among others), are being addressed intensively in current waves of research. The articles are accompanied by opinion articles from leaders in the field of CER and health policy in the USA, including AcademyHealth, the Agency for Healthcare Research and Quality and the PatientCentered Outcomes Research I nstitute. We hope that this issue will provide a valuable insight into the ARRA CER investment to date, and provide useful information for future investments. We very much welcome your comments on the current issue, and input and feedback for future features and articles.

How a unique provision in the American Recovery and Reinvestment Act set a foundation for the Patient-Centered Outcomes Research Institute.

In 2003, Tunis, Stryer and Clancy promoted the need for pragmatic clinical studies targeted to answer important clinical questions so that healthcare decision-makers could make informed choices among treatment options [1]. The call for greater emphasis on research that examines real-world clinical questions resulted in large part from frustration that traditional clinical research often excluded patients who have multiple conditions, are either young or old, live in hard-to-reach areas or are gender or racially diverse. The concept of ‘stakeholder-driven’ pragmatic research began to take hold as a rational approach to expand a research enterprise that sometimes seemed at odds with the needs of patients, their families, clinicians and payers. The Congressional Budget Office in 2007 issued a report that discussed the benefits of research comparing medical treatments, or comparative effectiveness research (CER) [2]. This report, along with the 2009 American Recovery and Reinvestment Act (ARRA), provided an extraordinary platform for conducting and building infrastructure for research that was explicitly designed to help healthcare decision-makers. ARRA provided an opportunity, never before realized, to develop research and a research platform that was stakeholder driven, pragmatically focused and targeted toward practical questions. ARRA dedicated an unprecedented US$1.1 billion to this effort, collectively involving the Agency for Healthcare Research and Quality (AHRQ), the NIH and the Office of the Secretary, Department of Health and Human Services. Many aspects of the legislation served as important foundations for the efforts of the future Patient-Centered Outcomes Research Institute (PCORI). One particular investment made by ARRA was to support the convening of an IOM committee to prioritize topics for CER. With the input of thousands of patients and other stakeholders, this committee issued a report, identifying and prioritizing 100 key CER topics, that continues to be a touchstone for identifying major topics for c omparative research. The legislation also created the Federal Coordinating Council for Comparative Effectiveness Research (FCCCER), which defined CER and developed a framework to guide the federal ARRA investment. One of FCCCER’s most notable outcomes was a broadening of CER to include prevention, delivery systems and behavioral change. This broadening was also apparent in the IOM 100 priority topics. CER is not, however, an entirely new concept. Well before ARRA, both NIH and AHRQ could point to important examples of comparative effectiveness studies. AHRQ in particular had begun implementing its mandate to fund CER by developing a stakeholder-driven, transparent research program. These efforts helped spark a movement for greater engagement of patients, caregivers, clinicians and other healthcare stakeholders in research. They not only formed a baseline for PCORI, but also an expectation that future clinical research would engage SPECIAL FOCUS y The ARRA investment in CER

The ARRA investment in CER: a description of the midstream evaluation and how the funds were allocated and CER priorities addressed.

To describe the evaluation design of the American Recovery and Reinvestment Act of 2009 comparative effectiveness research (CER) investment, how funds were allocated and how CER priorities were addressed. Primary and secondary data included information from redacted project proposals, an investigator survey and federal project officers, investigators and expert panel discussions. More than 420 projects (US$1.1 billion) were awarded. Those generating new or synthesizing existing CER made up the plurality (194, or US$524 million). Data infrastructure projects were the second-largest area (28%, US$302 million). More than three-fourths addressed at least one priority population, condition category or intervention category. These investments expanded the nation's CER activities and its future capacity to conduct CER.

What does US$1.1 billion buy? An investment in the future.

Journal of Comparative Effectiveness ResearchVol. 3, No. 6 OpinionWhat does US$1.1 billion buy? An investment in the futureLisa A SimpsonLisa A SimpsonE-mail Address: Lisa.Simpson@academyhealth.orgAcademyHealth, 1150 17th Street, NW, Suite 600, Washington, DC 20036, USASearch for more papers by this authorPublished Online:12 Dec 2014https://doi.org/10.2217/cer.14.74AboutSectionsView ArticleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinkedInRedditEmail View articleKeywords: academicCERfundingimpactincentivespolicyresearch infrastructureworkforceReferences1 National Academies of Science. Rising Above the Gathering Storm: Energizing and Employing America for a Brighter Economic Future. National Academy Press, Washington, DC, USA (2007).Google Scholar2 Institute of Medicine, National Academy of Sciences, and National Academy of Engineering. Rising Above the Gathering Storm: Energizing and Employing America for a Brighter Economic Future. The National Academies Press, Washington, DC, USA (2007). www.nap.edu/catalog/11463.html Google Scholar3 ResearchAmerica. America Speaks: poll data summary, Volume 10 (2009). www.researchamerica.org/uploads/AmericaSpeaksV10.pdfGoogle Scholar4 Wilensky GR. The policies and politics of creating a comparative clinical effectiveness research center. Health Aff. 28(4), w719–w729 (2009).Crossref, Google Scholar5 Orszag P. Time to act on health care costs. Issues in Science and Technology. 27 November 2013. http://issues.org/24–23/orszagGoogle Scholar6 Institute of Medicine. Initial National Priorities for Comparative Effectiveness Research. Institute of Medicine, Washington, DC, USA (2009).Google Scholar7 Conway PH, Clancy C. Comparative-effectiveness research – implications of the Federal Coordinating Council's report. N. Engl. J. Med. 361, 328–330 (2009).Crossref, Medline, CAS, Google Scholar8 Esposito D, Yong P, Rich E, Geonnotti K, Kimmey L. The ARRA investment in CER: a description of the midstream evaluation and how the funds were allocated and CER priorities addressed. J. Comp. Eff. Res. 3(6), 581–589 (2014).Link, Google Scholar9 Forrest C, Simpson L, Clancy C. Child health services research: challenges and opportunities. JAMA 277(22), 1787–1793 (1997).Crossref, Medline, CAS, Google Scholar10 Brownson RC, Colditz GA, Proctor EK. Dissemination and Implementation Research in Health: Translating Science to Practice. Oxford University Press, UK (2012).Crossref, Google Scholar11 A National Research Agenda for Public Health Services and Systems. Am. J. Prev. Med. 42(5S1), S72–S78 (2012). www.rwjf.org/content/dam/farm/articles/journal_articles/2012/rwjf72691Medline, Google Scholar12 Halfon N, Schuster M, Valentine W, McGlynn E. Improving the quality of healthcare for children: implementing the results of the AHSR research agenda conference. Health Serv. Res. 33(4 Pt 2), 955–976 (1998).Medline, CAS, Google Scholar13 Proctor EK, Landsverk J, Baumann AA et al. The implementation research institute: training mental health implementation researchers in the United States. Implement. Sci. 8, 105 (2013).Crossref, Medline, Google Scholar14 Hanson TA. Scientific communities of practice for learning – lessons from ethnographic fieldwork. www.academia.edu/1429397/Scientific_communities_of_practice_for_learning_-_lessons_from_ethnographic_fieldworkGoogle Scholar15 Holve E, Segal C, Rein A, Hamilton Lopez M, Johnson B. The Electronic Data Methods (EDM) Forum for comparative effectiveness research (CER). Med. Care 50, S7–S10 (2012).Crossref, Medline, Google Scholar16 Wilcox A, Randhawa G, Embi P, Cao H, Kuperman G. Sustainability Considerations for Health Research and Analytic Data Infrastructures. eGEMs 2(2), doi:10.13063/2327-9214.1113 (2014).Crossref, Google Scholar17 The International School on Research Impact Assessment. www.theinternationalschoolonria.comGoogle Scholar18 Guthrie S, Wamae W, Diepeveen S, Wooding S, Grant J. Measuring Research: A Guide to Research Evaluation Frameworks and Tools. Prepared for the Association of American Medical Colleges. http://www.rand.org/content/dam/rand/pubs/monographs/MG1200/MG1217/RAND_MG1217.sum.pdf Google Scholar19 American Society for Cell Biology. The San Francisco Declaration on Research Assessment. http://am.ascb.org/dora Google Scholar20 Bladek M. DORA: San Francisco Declaration on Research Assessment. C & R L News April: 191–196. http://crln.acrl.org/content/75/4/191.full.pdf+htmlGoogle Scholar21 Priem J. Scholarship: Beyond the paper. Nature 495(7442), 437–440 (2013).Crossref, Medline, CAS, Google Scholar22 AcademyHealth. AHRQ: 15 Years of transforming care and improving health (2014). www.academyhealth.org/files/AHRQReport2014.pdfGoogle Scholar23 Research Excellence Framework. Decisions on assessing research impact (2011). www.ref.ac.uk/media/ref/content/pub/decisionsonassessingresearchimpact/01_11.pdfGoogle Scholar24 Alivisatos P, Isaacs ED, Mason T. The sequester is going to devastate U.S. science research for decades. The Atlantic, March 2013. www.theatlantic.com/politics/archive/2013/03/the-sequester-is-going-to-devastate-us-science-research-for-decades/273925 Google Scholar25 Stein S. The next frontier in the war over science. Huffington Post, 6 May 2014. www.huffingtonpost.com/2014/05/06/war-on-science_n_5269527.htmlGoogle Scholar26 Inside Health Policy. House republicans say increased NIH, FDA funding off table for medical innovation bill. 10 September 2014. http://insidehealthpolicy.com/house-republicans-nih-fda-funding-bump-table-innovation-billGoogle ScholarFiguresReferencesRelatedDetailsCited ByA Half-Century of Change22 May 2017 | Health Services Research, Vol. 52, No. 3 Vol. 3, No. 6 Follow us on social media for the latest updates Metrics Downloaded 50 times History Published online 12 December 2014 Published in print November 2014 Information© Future Medicine LtdKeywordsacademicCERfundingimpactincentivespolicyresearch infrastructureworkforceFinancial & competing interests disclosureAcademyHealth received support from the ARRA funds, but these projects have since either ended or are now funded through other federal and non-federal sources. The author has no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.No writing assistance was utilized in the production of this manuscript.PDF download

Assessing the long-term impact of public investments in comparative effectiveness research: conceptual framework and lessons learned.

The American Recovery and Reinvestment Act (ARRA) of 2009 directed US$1.1 billion to the US Department of Health and Human Services for support of comparative effectiveness research (CER). As part of this investment, US Department of Health and Human Services commissioned a midstream evaluation of the ARRA CER portfolio. One goal of the evaluation was to identify issues to consider for a future evaluation of the long-term impact of this portfolio and other CER investments. In planning the ARRA CER evaluation, we developed and revised a conceptual framework and related policy research questions that may be useful to future efforts to assess the impact of CER or patient-centered outcomes research investments. In addition, we explored methodological challenges related to designing an evaluation to assess investments in CER that may be informative to any future plans to evaluate the long-term impact of ARRA CER as well subsequent investments made from the Patient-Centered Outcomes Research Trust Fund.

The Politics of Comparative Effectiveness Research: Lessons from Recent History

Efforts to support and use comparative effectiveness research (CER), some more successful than others, have been promulgated at various times over the last forty years. Following a resurgence of interest in CER, recent health care reforms provided substantial support to strengthen its role in US health care. While CER has generally captured bipartisan support, detractors have raised concerns that it will be used to ration services and heighten government control over health care. Such concerns almost derailed the initiative during passage of the health care reform legislation and are still present today. Given recent investments in CER and the debates surrounding its development, the time is ripe to reflect on past efforts to introduce CER in the United States. This article examines previous initiatives, highlighting their prescribed role in US health care, the reasons for their success or failure, and the political lessons learned. Current CER initiatives have corrected for many of the pitfalls experienced by previous efforts. However, past experiences point to a number of issues that must still be addressed to ensure the long-term success and sustainability of CER, including adopting realistic aims about its impact, demonstrating the impact of Patient-Centered Outcomes Research Institute (PCORI) and communicating the benefits of CER, and maintaining strong political and stakeholder support.

Doctor Knows Best: Physician Endorsements, Public Opinion, and the Politics of Comparative Effectiveness Research

The Obama administration has made a major investment in comparative effectiveness research (CER) to learn what treatments work best for which patients. CER has the potential to reduce wasteful medical spending and improve patient outcomes, but the political sustainability of this initiative remains unclear because of concerns that it will threaten the doctor-patient relationship. An unresolved question is whether it is possible to boost public support for the use of CER as a cost-control strategy. We investigate one potential source of public support: Americans' trust in physicians as faithful agents of patient interests. We conducted two national surveys to explore the public's confidence in doctors compared to other groups. We find that doctors are viewed as harder workers, more trustworthy, and more caring than other professionals. Through survey experiments, we demonstrate that the support of doctors' groups for proposals to control costs and use CER have a greater influence on aggregate public opinion than do cues from political actors including congressional Democrats, Republicans, and a bipartisan commission. Our survey results suggest that the medical profession's stance will be an important factor in shaping the political viability of efforts to use CER as a tool for health care cost control.

Dissemination and Implementation Research in Health (R21): PAR-13-054

Also note: Dissemination and Implementation Research in Health (R01): PAR-13-055 Details at: http://grants.nih.gov/grants/guide/pa-files/PAR-13-055.html. Also note: Dissemination and Implementation Research in Health (R03): PAR-13-056 Details at: http://grants.nih.gov/grants/guide/pa-files/PAR-13-056.html. Components of Participating Organizations...

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

Prioritization of translational research on genomic tests is critically important given the rapid pace of innovation in genomics. The goal of this study was to evaluate a stakeholder-informed priority-setting framework in cancer genomics. An external stakeholder advisory group including patients/consumers, payers, clinicians, and test developers used a modified Delphi approach to prioritize six candidate cancer genomic technologies during a 1-day meeting. Nine qualitative priority-setting criteria were considered. We used a directed, qualitative content-analysis approach to investigate the themes of the meeting discussion. Stakeholders primarily discussed six of the original nine criteria: clinical benefits, population health impacts, economic impacts, analytical and clinical validity, clinical trial implementation and feasibility, and market factors. Several new priority-setting criteria were identified from the workshop transcript, including "patient-reported outcomes," "clinical trial ethics," and "trial recruitment." The new criteria were incorporated with prespecified criteria to develop a novel priority-setting framework. This study highlights key criteria that stakeholders can consider when prioritizing comparative effectiveness research for cancer genomic applications. Applying an explicit priority-setting framework to inform investment in comparative effectiveness research can help to ensure that critical factors are weighed when deciding between many potential research questions and trial designs.

Private Manufacturersʼ Thresholds to Invest in Comparative Effectiveness Trials

The recent rush of enthusiasm for public investment in comparative effectiveness research (CER) in the US has focussed attention on these public investments. However, little attention has been given to how changing public investment in CER may affect private manufacturers' incentives for CER, which has long been a major source of CER. In this work, based on a simple revenue maximizing economic framework, we generate predictions on thresholds to invest in CER for a private manufacturer that compares its own product to a competitor's product in head-to-head trials. Our analysis shows that private incentives to invest in CER are determined by how the results of CER may affect the price and quantity of the product sold and the duration over which resulting changes in revenue would accrue, given the time required to complete CER and the time from the completion of CER to the time of patent expiration. We highlight the result that private incentives may often be less than public incentives to invest in CER and may even be negative if the likelihood of adverse findings is sufficient. We find that these incentives imply a number of predictions about patterns of CER and how they will be affected by changes in public financing of CER and CER methods. For example, these incentives imply that incumbent patent holders may be less likely to invest in CER than entrants and that public investments in CER may crowd out similar private investments. In contrast, newer designs and methods for CER, such as Bayesian adaptive trials, which can reduce ex post risk of unfavourable results and shorten the time for the production of CER, may increase the expected benefits of CER and may tend to increase private investment in CER as long as the costs of such innovative designs are not excessive. Bayesian approaches to design also naturally highlight the dynamic aspects of CER, allowing less expensive initial studies to guide decisions about future investments and thereby encouraging greater initial investments in CER. However, whether the potential effects we highlight of public funding of CER and of Bayesian approaches to trial design actually produce changes in private investment in CER remains an empirical question.

Five Reasons That Many Comparative Effectiveness Studies Fail To Change Patient Care And Clinical Practice

Despite widespread enthusiasm about the potential impact of new investments in comparative effectiveness research, recent history suggests that scientific evidence may be slow to change clinical practice. Reflecting on studies conducted over the past decade, we identify five causes that underlie the failure of many comparative effectiveness studies to alter patient care. These are financial incentives, such as fee-for-service payment, that may militate against the adoption of new clinical practices; ambiguity of study results that hamper decision making; cognitive biases in the interpretation of new information; failure of the research to address the needs of end users; and limited use of decision support by patients and clinicians. Policies that encourage the development of consensus objectives, methods, and evidentiary standards before studies get under way and that provide strong incentives for patients and providers to use resources efficiently may help overcome at least some of these barriers and enable comparative effectiveness results to alter medical practice more quickly.

Can Genomics Bend the Cost Curve?

IN 1960, US HEALTH CARE EXPENDITURES TOTALED SLIGHTLY more than $100 per person and 5% of the gross domestic product (GDP). Now in 2012, individual health care expenditures are more than $8000 per year and the proportion of the GDP spent on health care is more than 17%. Health care costs are widely believed to contribute to the economic challenges faced by the public and private sectors of the US economy. Calls to bend the health care cost curve have become headline news. Prior approaches to slowing the growth in US health care costs have had limited success. The use of cost-benefit analyses to make coverage or clinical decisions has proven socially and politically unacceptable in the United States. Managed care temporarily slowed cost increases in health care but the public backlash against restriction of choice undermined its potential long-term effects. Similarly, the unwillingness to include economic outcomes in the current investment in comparative effectiveness research will limit its effects on health care costs. New approaches are needed. At the same time that health care costs have gained increasing attention, advances in genomics have begun to have a major influence on clinical care, particularly in oncology. Germline and somatic genetic variation is increasingly used to predict disease incidence, severity, prognosis, and response to therapy in clinical practice as well as to provide new therapeutic targets in the laboratory. Just as with infectious epidemics, this new ability to understand host factors may prove a useful approach to managing a cost epidemic, even beyond the focus on the agent (ie, the health care intervention) or the vector (ie, the health care clinician). There are 2 perspectives on the effect of genomics on health care costs. One view is that genomics is a form of new technology and it is well established that new technologies increase health care costs. For example, identification of the ERBB2 HER2/neu (formerly) gene and the development of trastuzumab has increased the cost of breast cancer care, in part because of the additional cost of ERBB2 expression testing (approximately $150), but mostly because of the cost of trastuzumab (approximately $45 000) for the 20% to 30% of women who test ERBB2-positive. The total cost of this genomic advance, with substantial clinical benefit, has been estimated at more than $750 million per year. Other examples include the implantation of defibrillators in patients who are found to carry a mutation linked to a hereditary cardiomyopathy or the use of breast magnetic resonance imaging screening for women with a BRCA mutation. From this perspective, advances in genomics will extend life but exacerbate the current growth in health care expenditures. However, genomics differs from many other health care technologies because it can also be used to identify individuals who will experience little to no benefit from an intervention, either because they are at low risk of having an adverse outcome without intervention or because they will not respond to the intervention. Reducing the use of interventions that will have little to no benefit could have an important effect on the cost curve, particularly when the intervention is common or very costly. One example is the use of gene expression profiling among women with breast cancer. A 21 gene expression panel correlates with the benefit of adjuvant chemotherapy among women with localized, estrogen receptor–positive breast cancer such that women with a low-risk score receive little to no benefit from chemotherapy. Several studies have demonstrated that use of the gene expression panel leads to lower rates of chemotherapy among women with low-risk scores. Furthermore, because the low-risk group is 2 to 3 times larger than the high-risk group, overall rates of adjuvant chemotherapy would decline by 15% to 20%. Assuming adjuvant chemotherapy incurs approximately $20 000 in health care costs per woman and 100 000 women are diagnosed with localized, estrogen receptor–positive breast cancer each year, the use of gene expression profiling in this setting has the potential to save $400 million each year. Likewise, other genomic tests help to predict the response to an expensive therapy, including the use of epidermal growth factor receptor testing to determine the use of erlotinib in advanced non–small cell lung cancer or the use of KRAS testing to determine the use of cetuximab in advanced colorectal cancer. While these examples are only a tiny fraction of health care costs, this fraction could increase if “omic” information proves useful for identifying

Comparative Effectiveness Research: An Empirical Study of Trials Registered in ClinicalTrials.gov

BackgroundThe $1.1 billion investment in comparative effectiveness research will reshape the evidence-base supporting decisions about treatment effectiveness, safety, and cost. Defining the current prevalence and characteristics of comparative effectiveness (CE) research will enable future assessments of the impact of this program.MethodsWe conducted an observational study of clinical trials addressing priority research topics defined by the Institute of Medicine and conducted in the US between 2007 and 2010. Trials were identified in ClinicalTrials.gov. Main outcome measures were the prevalence of comparative effectiveness research, nature of comparators selected, funding sources, and impact of these factors on results.Results231 (22.3%; 95% CI 19.8%–24.9%) studies were CE studies and 804 (77.7%; 95% CI, 75.1%–80.2%) were non-CE studies, with 379 (36.6%; 95% CI, 33.7%–39.6%) employing a placebo control and 425 (41.1%; 95% CI, 38.1%–44.1%) no control. The most common treatments examined in CE studies were drug interventions (37.2%), behavioral interventions (28.6%), and procedures (15.6%). Study findings were favorable for the experimental treatment in 34.8% of CE studies and greater than twice as many (78.6%) non-CE studies (P<0.001). CE studies were more likely to receive government funding (P = 0.003) and less likely to receive industry funding (P = 0.01), with 71.8% of CE studies primarily funded by a noncommercial source. The types of interventions studied differed based on funding source, with 95.4% of industry trials studying a drug or device. In addition, industry-funded CE studies were associated with the fewest pediatric subjects (P<0.001), the largest anticipated sample size (P<0.001), and the shortest study duration (P<0.001).ConclusionsIn this sample of studies examining high priority areas for CE research, less than a quarter are CE studies and the majority is supported by government and nonprofits. The low prevalence of CE research exists across CE studies with a broad array of interventions and characteristics.

Can Coverage Be Rescinded When Negative Trial Results Threaten A Popular Procedure? The Ongoing Saga Of Vertebroplasty

To attain the anticipated benefits of increased investments in comparative effectiveness research, the results must translate into improved medical practice and policy. In this article we present an analysis of a case study of percutaneous vertebroplasty, a widely used invasive procedure to treat painful vertebral fractures by injecting bone cement into the spine. In August 2009, results from a pair of rigorous double-blind randomized controlled trials were published and reported that vertebroplasty provided no better pain relief than a sham procedure in which needles were introduced into the back without injecting cement. More than two years after publication of the two studies, insurers' coverage of the procedure continues unchanged. This raises serious questions about the policy mechanisms that exist in the United States to interpret and act upon "negative" research findings from studies of popular health care interventions.

Something old, something new, something borrowed...comparative effectiveness research: a policy perspective.

In the nearly 18 months that have elapsed following enactment of the Patient Protection and Affordable Care Act (PPACA), health care scholars have debated a key portion of the legislation that has the potential to dramatically impact health care. Section 6301 of the PPACA outlines how the federal government will play an increasingly direct role in shaping comparative effectiveness research (CER).1 The inclusion of CER in the PPACA marked a critical step in the advancement of health services research, given that the legislation called for significant federal investment in CER with the ultimate goal of improving the efficiency of the health care system. Here, we briefly review the policy history of CER as it relates to prior efforts in health services research and health technology assessment, discuss the promises and fears associated with CER as outlined in the PPACA, and highlight the important role that the new Patient-Centered Outcomes Research Institute (PCORI) will likely play in advancing both CER methods and evidence generation.

Infrastructure requirements for secondary data sources in comparative effectiveness research.

The growing interest in comparative effectiveness research (CER) has re-ignited the debate about the inadequacy of data from randomized controlled trials (RCTs) to address patient-centered decision making. Despite their well-known internal validity and use as the gold standard for regulatory decision making, the limitations of RCTs are widely recognized. In addition to their lack of statistical power due to inadequate sample size to address certain research hypotheses, practical and ethical considerations may preclude their viability. A case in point is the ethical dilemma in conducting an RCT to establish whether a diet high in fat content may be a risk factor for dementia, which might produce useful public health information but would not be acceptable in terms of protection of human subjects. Frequently, RCTs provide substantial information regarding the efficacy of drugs and other medical interventions, yet leave large gaps in evidence that would be relevant for medical decision making. Even when RCTs are carried out with this intent, they may not necessarily reflect “real-world” experience and, therefore may not provide sufficient evidence to guide patient-centered care. The substantial investment in CER and the broad objective implied in the American Recovery and Reinvestment Act of 2009 have necessitated the need to seek alternative sources of data to meet the emerging health care questions. The stated requirements include an “.... assessment of a comprehensive array of health-related outcomes for diverse patient populations and sub-groups” as well as “... a wide range of interventions;” and “[d]evelopment, expansion, and use of a variety of data sources and methods to assess comparative effectiveness.”1-2 Contending with the changes in health care policy and delivery clearly requires doing things differently, as discussed at a recent workshop sponsored by the Institute of Medicine.3 The workshop summary highlighted the dependence on clinical trials as the “sole source of evidence on the constantly accelerating flow of diagnostic and treatment challenges is unfeasible.” The need for a “learning healthcare system” with “real-time learning from the clinical experience and seamless application of the lessons in the care process” was emphasized. Secondary data, such as registries and retrospective databases, can be used to complement RCTs, since they are less costly and can be used to incorporate real world experience to answer questions. Further, important questions, such as adherence, treatment patterns, and burden of disease, can be answered in retrospective analyses of databases. However, effective use of secondary data requires addressing major methodological and infrastructural issues that may be related to, but often go beyond, those encountered with most RCTbased work. Infrastructural issues, such as tools to efficiently access and correctly analyze the data, need to be developed for effective use of such data sources. Guidelines need to be formulated and data standards established using RCTs as a role model. Data warehouses are also required to be established that respect the privacy and confidentiality of patients. In this paper, we discuss the infrastructural requirements for secondary data utilization in CER, and identify gaps that must be filled to address the underlying issues, with emphasis on data standards, data quality assurance, data warehouses, computing environment, and protection of privacy and confidentiality.

Pediatric hospitalist systems versus traditional models of care: Effect on quality and cost outcomes

Pediatric hospitalist systems are increasing in popularity, but data regarding the effects of hospitalist systems on the quality of care has been sparse, in part because rigorous metrics for analysis have not yet been established. We conducted a literature review of studies comparing the performance of pediatric hospitalists and traditional attendings. To determine the effect of pediatric hospitalists on quality and outcome metrics such as length of stay, cost, patient satisfaction, mortality, readmission rates, and use of evidence-based medicine during care. A Medline literature search identified 11 studies that met criteria for inclusion. Five previously reviewed studies reported lengths of stay between 6% and 14% shorter for hospitalists. Five of the new studies evaluated lengths of stay, with 1 showing significantly lower length of stay and cost for a faculty model, 1 showing lower length of stay for hospitalists for all conditions, 1 for certain conditions only, and 2 showing no statistical difference. Six studies reported on readmission rate, with 4 showing no difference, 1 showing decreased readmissions for hospitalists, and 1 showing decreased readmissions for a traditional faculty service. Hospitalists self-report higher use of evidence-based guidelines. Few differences in patient satisfaction were reported. Mortality on the pediatrics wards is rare, and no studies were adequately powered to evaluate mortality rate. Hospitalists can improve the quality and efficiency of inpatient care in the pediatric population, but the effect is not universal, and mechanisms underlying demonstrated improvements are poorly understood. We propose 4 components to improve quality and value in hospital medicine systems: investment in comparative effectiveness research involving delivery system interventions, development and implementation of pediatric quality measures, better understanding of improvement mechanisms for hospital medicine systems, and increased focus on quality and value delivered by hospital medicine groups and individuals.

Evaluating the effectiveness of interventions to control noise and work-related hearing loss.

The objective of this presentation is to discuss the literature and recommendations on the evaluation of the effectiveness of interventions to control noise and prevent hearing loss. The American Recovery and Reinvestment Act of 2009 included a provision for federal funding to investigate how different interventions stack up against each other. The Act called on the Institute of Medicine to recommend a list of priority topics to be the initial focus of a new national investment in comparative effectiveness research. The need for research on hearing loss was placed in the highest priority group. Two recent Cochrane Reviews addressed the effects of interventions for the prevention of work-related hearing loss. Those concluded that some interventions improve the mean use of hearing protection devices compared to non-intervention; that there is low quality evidence that legislation can reduce noise levels in workplaces and contradictory evidence that prevention programs are effective in the long-term. There is consensus that most interventions focus on the use of hearing protection devices, and effectiveness depends on the quality of the implementation. Even though case studies show that substantial noise control can be achieved in the workplace, there is no evidence of this practice in the scientific literature.