Redesigning the Clinical Effectiveness Research Paradigm Innovation and Practice-Based Approaches: Workshop Summary (2010) / Chapter Skim
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5 Moving to the Next Generation of Studies
5 Moving to the Next Generation of Studies
Pages 267-322
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A variety of innovations are presented as important components of a redesigned research paradigm as well as immediate opportunities to build toward a next generation of studies. These innovations include new approaches to observational and hybrid studies; tools for collecting and using information captured at the point of care, including those relevant to genetic variation; cooperative research networks; and possible incentives.
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John Rush from the University of Texas Southwestern Medical Center proposes that researchers widen the breadth of study designs that they employ. Rush illustrates how certain clinically important questions can be addressed with observational data obtained when systematic practices are employed, or with new study designs (e.g., hybrid studies and equipoise stratified randomized designs)
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Eric B Larson from the Group Health Cooperative concludes the chapter by suggesting that emerging research networks, such as the development of programs funded by the National Institutes of Health (NIH)
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The corresponding lack of geographic variation in patient outcomes prompted research using administrative data enhanced with clinical data to assess the quality of medical care. The number and type of quality measures reported on healthcare providers, such as hospitals, nursing homes, physicians, and health plans, have grown substantially over the past decade (Byar, 1980)
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New tools, beyond those that expedite the mechanics of searching and accessing information, are required. Using Diverse Data Streams A fundamental problem of using diverse data sources is that of poolability.
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In looking forward, however, increasing data pooling should provide more information. Using Observational Data to Enhance Clinical Trial Data The use of observational data to supplement a randomized trial is not a new idea, and there exists a large literature describing advantages and disadvantages.
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2004. Bayesian approaches to clinical trials and health-care evaluation.
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bitmap image estimates of effectiveness and safety. A selection of types of data streams for DES includes device, procedure, patient characteristics and outcomes, as displayed in Figure 5-3.
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New study designs are needed that exploit features of diverse information sources. There is some experience in pooling observational data with clinical trial data.
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While findings of observational studies are intrinsically more prone to uncertainty than those from randomized trials, at present many of these investigations have suboptimal methodology, which can be corrected. Common problems include elementary design errors; failure to identify a clinically meaningful t0, or start of follow-up; exposure and disease misclassification; use of overly broad end-points for safety studies; confounding by the healthy drug user effect; and marginal sample size.
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Observational studies also have E+ and E– groups, follow-up commences at a certain t0, and individuals are followed forward in time to determine end-points; however, there are some important differences. First, the exposure group (E)
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2003. Evaluating medication effects outside of clinical trials: new-user designs.
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. Although some advocate the use of intention to treat as in the conduct of clinical trials, in observational studies, there is not necessarily an intention to treat, maintain treatment, or promote adherence as in an RCT, so adherence rates may be low and discontinuation rates may be very high.
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Finally, it is time to train a generation of epidemiologists to be more familiar with the clinical and pharmacological principles that affect the use of observational data. This expertise will allow clinicians to better exploit the wealth of available observational data and will lead to improve study designs.
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This report illustrates how some of these clinically important questions can be addressed with observational data obtained when systematic practices are employed, or with new study designs (e.g., hybrid studies, equipoise stratified randomized designs) or post hoc (e.g., moderator)
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I suggest that T1 translational research should be called Translational Research and that T2 research be renamed to Applications Research and divided into Clinical Implementation, Dissemination, and Systems/Policy research to further specify these different research enterprises, as implied by Woolf (2008)
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Figure 5-7 suggests a conceptual map of the key factors that affect FIGURE 5-7 T2 Translational research. Figure 5-7.eps bitmap image
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multisite Sequenced Treatment Alternatives to Relieve Depression (STAR*
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. Clinical results for patients with major depressive disorder in the Texas Medication Algorithm Project.
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, the "for whom" question is never addressed in efficacy trials, perhaps in part because efficacy trials enroll symptomatic volunteers with little or no co-morbid psychiatric or general medical pathology, with minimal chronicity and treatment resistance (i.e., prior failed treatment trials) (Table 5-1)
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bitmap image TABLE 5-1 Population Gaps Parameter Symptomatic Volunteers Typical Patients Chronically ill – +++ Concurrent Axis I + +++ Concurrent Axis III + +++ Treatment-resistant + ++ Suicidal – ++ Substance abusing – ++ Will accept placebo + + 0.47 (for three) and 0.52 (for four or more)
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Such effectiveness studies require large samples and simple outcomes -- so-called Practical Clinical Trials (March et al., 2005; Tunis et al., 2003)
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. Then, by using randomized treatment assignment in the second, third, and fourth treatment steps, we could isolate which of several different treatment options would be best for patients for whom one, two, or three prior treatments (each provided in the study itself)
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Other Designs Finally, a comment about other study designs is in order -- in particular adaptive designs (Murphy et al., 2007; Pineau et al., 2007) and equipoise stratified randomized designs (Lavori et al., 2001)
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. A range of study designs (registry/cohort studies, effectiveness, hybrid, adaptive, and ESRD)
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could be released and contracts let to address these questions in a timely and focused fashion. A significant annual financial commitment from the relevant institutes should be made to Clinical Implementation Research (T2)
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With systems of care now using electronic medical records, large practical clinical trials are feasible. One major hurdle remains: how to select the most important questions for prospective study to ensure results will change practice, enhance outcomes, improve cost efficiency, and/or make treatments safer.
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Fortunately, the informational by-products of routine clinical care can be used to bring phenotyping and sample acquisition to the same highthroughput, commodity price-point as is currently true of genotyping costs. The National Center for Biomedical Computing, Informatics for Integrating Biology to the Bedside (i2b2)
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bitmap image to include the appropriate sample size. Rather than sample sizes such as 100 or even 1,000 patients, as in these 13 underpowered studies, research will require populations on the order of 10,000 patients; (2)
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The remainder of my discussion will focus on efforts to bring greater efficiency and affordability to the processes of phenotyping and sample acquisition and, in particular, on several new open source tools that aim to help the healthcare enterprise better capture the information and bioproducts produced during the course of clinical care such that they can be used effectively for discovery research. An important component of any analysis is being able to obtain the "right" populations though phenotyping.
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Because for this type of analysis, billing codes are too coarse grained and biased, we used automated natural language processing to evaluate text of doctors' notes in online health records. Improving this technique to the point that it was useful was quite challenging; but, ultimately we were able to quickly, reproducibly, and accurately stratify 96,000 out of 2.5 million patients for disease severity, pharmacoresponsiveness, and exposures.
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This type of safe harbor has led to innovation in computational methodologies. Yet these types of challenges and safe harbors do not exist for equally complex areas in clinical medicine -- such as predicting risk of recurring breast cancer (e.g., the Oncotype or MammaPrint gene expression tests)
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sponsored randomized clinical trials evaluating high-dose chemotherapy with autologous bone marrow transplantation (HDC/ABMT) compared to conventional-dose chemotherapy for the treatment of metastatic breast cancer.
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The two pathways or "systems" of use that emerged can be characterized as a (1) a "rational system" of evaluation -- emphasizing systematic evaluation of evidence by technology assessments, clinical practice guidelines, and randomized clinical trials -- and (2)
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In this specific example, BCBSA created a mechanism or Demonstration Project outside of coverage that would allow its plans to participate in randomized trials evaluating the effects of HDC/ABMT on breast cancer. This was essentially a new organization housed at BCBSA in Chicago, which developed contracts with providers and health plans to cover patient care costs "outside of usual coverage and medical necessity provisions" for eligible BCBS Plan enrollees.
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State mandates for coverage of HDC/ABMT for breast cancer, which numbered 15 during the mid-1990s, proved to be ill advised, as they circumvented the results of technology assessments (which showed evidence gaps) and contributed to the delay in the completion of the NCI randomized clinical trials.
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This was followed by the Medicare Clinical Trials Policy in 2000 that expanded Medicare coverage for qualifying clinical trials. Finally, coverage with evidence development was formalized in a CMS guidance document in 2006 (CMS coverage website)
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By designing practical clinical trials (Tunis et al., 2003) comparing new health interventions to relevant or existing alternatives in conjunction with CED, an attempt is being made to find an optimal balance between innovation, access, evidence, and efficiency in practice.
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aim to identify evidence gaps of important technologies for conditions having a significant burden of illness or cost, using systematic reviews, technology assessments, and other types of research. One such AHRQ-funded project at the CMTP started with a Stanford EPC comparative effectiveness review of existing research comparing percutaneous coronary intervention (PCI)
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Historically, the Demonstration Project mechanism at BCBSA for BCBS Plan support of patient care costs for HDC/ABMT breast cancer patients outside of routine coverage addressed this issue successfully. Recently, a conceptual framework for CED with model benefit language has been developed by the CMTP as an applied policy project through a grant from the California HealthCare Foundation (Center for Medical Technology Policy, 2009)
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Sarah Greene, M.P.H. Group Health Center for Health Studies Fulfilling the Potential of the Learning Healthcare System Through Emerging Research Networks Recent publications have acknowledged and described the limitations of our current health research enterprise (Emanuel et al., 2004; Gawande, 2002, 2007; Lenfant, 2003; Tunis et al., 2003; Zerhouni, 2005b)
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Taken together, the learning healthcare system and a redesigned paradigm for clinical effectiveness research hold high promise to help to meet these challenges. A proposal to redesign the clinical effectiveness research paradigm for a learning healthcare system could draw inspiration from several existing models.
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appeared effective at reducing morbidity from acute and chronic diseases in carefully conducted clinical trials, but were then reported to have dramatic adverse consequences when translated into practice (Katzan et al., 2000)
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Research networks embedded in health plans have conducted RCTs and quasi-experimental studies of computerized physician order entry (CPOE) systems, including studies of various types of alerts and "academic detailing" (one-on-one education about use -- and often overuse -- of treatments such as medications)
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have been revealed to be too dangerous for general use because of inadequacies in the original efficacy and effectiveness research. Both researchers and clinicians realize the across-the-board risk to the clinical system and research enterprise of not anticipating and addressing these quality problems.
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What Is Our Vision to Guide the Next Generation of Studies and Exploit the Natural Advantages of Research Networks in Functioning Integrated Care-Delivery Systems? Three general principles underlie our vision: 1.
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and Mayo Clinic) are quite useful for time series analyses, correlations, and quasi-experimental research using observational data generated from clinical practice -- especially so-called natural experiments that occur as practice changes are instituted or external environment changes affect medical care and outcomes.
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Directly observing the dissemination of key clinical findings in practice also provides an effective window on translation. The up-to-the-minute, comprehensive data systems of these research networks lend themselves to examining changes in treatment, such as the use of aromatase inhibitors for adjuvant breast cancer therapy following reports of this successful therapeutic approach among referral populations in cancer trials (Aiello et al., 2008)
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workshop should aspire to inform the NIH's CTSA program. Indeed, the IOM's proposed redesign of the clinical effectiveness research paradigm ideally would address challenges the NIH will face as it aims to re-engineer the massive biomedical research enterprise we currently enjoy in the United States.
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Emerging research networks can form a reliable basis for such learning healthcare systems, which have the potential not only to accelerate the translation of research but also to ensure that it confers true benefits to patients and the public health. REFERENCES Aiello, E
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1980. Why data bases should not replace randomized clinical trials.
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2002. Mediators and mod erators of treatment effects in randomized clinical trials.
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1999. Linking efficacy and effectiveness research in the evaluation of psychotherapies.
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2004. Bayesian Approaches to Clinical Trials and Health-care Evaluation.
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2003. Practical clinical trials: Increasing the value of clinical research for decision making in clinical and health policy.
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U.S. biomedical research: Basic, translational, and clinical sciences.
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