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Taking Advantage of New Tools and Techniques

INTRODUCTION

As with virtually every scientific endeavor, clinical effectiveness research can be improved and expedited through innovation. In this case, innovation means the better use of existing tools and techniques as well as the development of entirely new methods and approaches. Understanding these emerging tools and techniques is critical to the discussion of improvements to the clinical effectiveness research paradigm. Better tools and enhanced techniques are fundamental building blocks in redesigning the clinical effectiveness paradigm, and new methods and strategies for evidence development are needed to use these tools to capture and analyze the increasingly complex information and data generated. In turn, better evidence will lead to stronger clinical and policy decisions and set the stage for further research.

Opportunities provided by developments in health information technology are reviewed in Chapter 4. In this chapter we review innovative uses of existing research tools as well as emerging methods and techniques. Part of the reform needed to enhance clinical effectiveness research is a more widespread understanding of different research tools and techniques, including greater clarity about what each can offer the overall research enterprise, both alone and in synergy with other approaches. A further need is broad, substantive support for ongoing development of new approaches and applications of existing tools and techniques that researchers believe may offer more benefits. As noted in Chapter 1, greater attention is needed to understand which approach is best suited for which situation and under what circumstances.



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3 Taking Advantage of New Tools and Techniques INTRODUCTION As with virtually every scientific endeavor, clinical effectiveness research can be improved and expedited through innovation. In this case, innova- tion means the better use of existing tools and techniques as well as the development of entirely new methods and approaches. Understanding these emerging tools and techniques is critical to the discussion of improvements to the clinical effectiveness research paradigm. Better tools and enhanced techniques are fundamental building blocks in redesigning the clinical effec- tiveness paradigm, and new methods and strategies for evidence development are needed to use these tools to capture and analyze the increasingly complex information and data generated. In turn, better evidence will lead to stronger clinical and policy decisions and set the stage for further research. Opportunities provided by developments in health information tech- nology are reviewed in Chapter 4. In this chapter we review innovative uses of existing research tools as well as emerging methods and techniques. Part of the reform needed to enhance clinical effectiveness research is a more widespread understanding of different research tools and techniques, including greater clarity about what each can offer the overall research enterprise, both alone and in synergy with other approaches. A further need is broad, substantive support for ongoing development of new approaches and applications of existing tools and techniques that researchers believe may offer more benefits. As noted in Chapter 1, greater attention is needed to understand which approach is best suited for which situation and under what circumstances. 

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 REDESIGNING THE CLINICAL EFFECTIVENESS RESEARCH PARADIGM The papers included in this chapter offer observations on improvements needed in the design and interpretation of intervention trials; methods that take better advantage of system-level data; possible improvements in analytic tools, sample size, data quality, organization, and processing; and novel techniques that researchers are beginning to use in conjunction with new information, models, and tools. Citing models from Duke University, The Society of Thoracic Surgeons (STS), and the Food and Drug Administration’s (FDA’s) Critical Path Clini- cal Trials Transformation Initiative, Robert M. Califf from Duke University discusses opportunities to improve the efficiency of clinical trials and to reduce their exorbitant costs. Innovations in the structure, strategy, con- duct, analysis, and reporting of trials promise to make them less expensive, faster, more inclusive, and more responsive to important questions. Particular attention is needed to identify regulations that improve clinical trial quality and eliminate practices that increase costs without an equal return in value. Finally, establishing “envelopes of creativity” in which innovation is encour- aged and supported is essential to maximizing the appropriate use of this methodology. Confounding is often the biggest issue in effectiveness analyses of large databases. Innovative analytic tools are needed to make the best use of large clinical and administrative databases. Sebastian Schneeweiss from Harvard Medical School observes that instrumental variable analysis is an underused, but promising, approach for effectiveness analyses. Recent developments of note include approaches that exploit the concepts of proxy variables using high-dimensional propensity scores and provider variation in prescribing preference using instrumental variable analysis. Rejecting any suggestion that “one trial = all trials,” Donald A. Berry from the University of Texas M.D. Anderson Cancer Center makes the case that adaptive and, particularly, Bayesian approaches lend themselves well to synthesizing and combining sources of information, such as meta-analyses, and provide means of modeling and assessing sources of uncertainty appro- priately. Therefore, Berry asserts, they are ideally suited for experimental trial design. Mark S. Roberts of the University of Pittsburgh, representing Archimedes Inc. at the workshop, suggests that physiology-based simulation and predic- tive models, such as an eponymous model developed at Archimedes, have the potential to augment and enhance knowledge gained from randomized controlled trials (RCTs) and can be used to fill “gaps” that are difficult or impractical to answer using clinical trial methods. Of particular relevance is the potential for these models to perform virtual comparative effectiveness trials. This chapter concludes with a discussion of the dramatic expansion of information on genetic variation related to common, complex disease and

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 TAKING ADVANTAGE OF NEW TOOLS AND TECHNIQUES the potential of these insights to improve clinical care. Teri A. Manolio of the National Human Genome Research Institute reviews recent find- ings from genomewide association studies that will enable examination of inherited genetic variability at an unprecedented level of resolution. She proposes opportunities to better capture and use these data to understand clinical effectiveness. INNOVATIVE APPROACHES TO CLINICAL TRIALS Robert M. Califf, M.D. Vice Chancellor for Clinical Research Duke University As we enter the era in which we hope that “learning health systems” (IOM, 2001) will be the norm, the evolution of randomized controlled trials required to meet the tremendous need for high-quality knowledge about diagnostic and therapeutic interventions has emerged as a critical issue. All too often, discussion about medical evidence gravitates toward a com- parison of randomized controlled trials and studies based on observational data, rather than toward a serious examination of ways to improve the operational methods of both approaches. My own experience in assessing the relative merits of RCTs versus observational studies dates back more than 25 years (Califf and Rosati, 1981), and recent discussions on this topic remind me of conversations I had as a medical student in 1977 with Eugene Anson Stead, Jr., M.D., the former chair of the Department of Medicine at Duke University. Dr. Stead founded the Duke Cardiovascular Disease Data- base, which eventually evolved into the Duke Clinical Research Institute; he is credited with helping change cardiovascular medicine from a discipline largely based on anecdotal observation to one based on clinical evidence. Dr. Stead, who was significantly ahead of his time, introduced us to a device not yet in common use—the computer—and urged us to record outcomes data on all of our patients. Further, he stressed that simply collecting infor- mation on acute, hospital-based practice was not sufficient; instead, we should add to this computerized collection throughout our patients’ lives. I firmly believe that this approach—building human systems that take advantage of the power of modern informatics—is the key to improving both RCTs and observational studies. Within the domain of clinical trials, an informatics-based approach holds promise both for pragmatic trials in broad populations, as well as in proof-of-concept (POC) trials intended to elucidate complex biological effects in small groups of people. In 1988, our research group published a paper in which we concluded that well-designed and carefully executed observational studies could pro- vide research data that were comparable in quality to those provided by

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 REDESIGNING THE CLINICAL EFFECTIVENESS RESEARCH PARADIGM RCTs (Hlatky et al., 1988). We have learned much since then, a point recently driven home during rounds in the Duke Coronary Care Unit (CCU). Time after time, we were faced with decisions that, had there had been a trial with an inception time for enrollment that coincided with the time point when we needed to make that clinical decision, the trial would likely have provided invaluable information for our CCU deliberations. While observational studies can provide useful knowledge, they are inadequate for detecting modest differences in effects between treatments (Peto et al., 1995), because without a common inception point and ran- domization to equally distribute known and unknown confounding factors, the risk of an invalid answer is substantial (DeMets and Califf, 2002a, 2002b). Innovation in clinical trials, in my view, is mostly concerned with performing them in optimal fashion, so that more knowledge is created more efficiently. How Can We Foster Quality in Clinical Trials? The most urgently needed innovation in implementing clinical trials is a more intelligent approach to defining and producing quality. Since random- ization is such a powerful tool for creating a basis to compare alternatives from a common inception point, we should abandon the assumption that the common critiques of RCTs stem from unalterable rules governing the conduct of such trials. Clinical trials are not required of their nature to be expensive, slow, noninclusive, and irrelevant to measurement of outcomes that matter to patients and medical decision makers. While innovative statistical methods have provided exciting additions to our capabilities, the main source of innovation in trials must be a focus on the fundamental “blocking and tackling” of clinical trials. A Structural Framework for Clinical Trials We have published a model, shown in simplified form in Figure 3-1, which integrates quantitative measurements of quality and performance into the development cycle of existing and future therapeutics (Califf et al., 2002). Such a model can serve as a basic approach to the development of reliable knowledge about medical care that is necessary but not sufficient for those wishing to provide the best possible care for their patients. Cur- rently, it takes too long to complete this cycle, but if we had continuous, practice-based registries and the ability to randomize within those regis- tries, we could see in real time which patients were included and excluded from trials. Further, upon completing the study, we could then measure the uptake of the results of the trial in practice. Such an approach provides a

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9 TAKING ADVANTAGE OF NEW TOOLS AND TECHNIQUES 3 2 Data 4 NIH Roadmap 1 Standards Network FDA Informat ion Early Early Critical Path Translational Tr 5 Steps Empirical Discover y Science Ethics 6 Priorities and Processes Clinical Measurement Outcomes Trials 12 and 7 Transparency Education to Consumers Inclusiveness 11 Clinical 8 Performance Pay for Practice Use for Performance Measures Guidelines Feedback on Priorities 9 10 Conflict of Interest Evaluation of Speed Management and Fluency FIGURE 3-1 Innovation in clinical trials: relevance of evidence system. SOURCE: Copyrighted and published by -1.eps HOPE/Health Affairs as Califf, Figure 3 Project landscape R. M., R. A. Harrington, L. K. Madre, E. D. Peterson, D. Roth, and K. A. Schulman. 2007. Curbing the cardiovascular disease epidemic: Aligning industry, government, payers, and academics. Health Affairs (Millwood) 26(1):62-74. The published ar- ticle is archived and available online at www.healthaffairs.org. system wherein everyone contributes to the registry and the results of trials are fed back into the registry in a rapid cycle. We have invested considerable efforts in evaluating the details of the system for generating clinical evidence from the perspective of cardiovascu- lar medicine, where there is a long history of applying scientific discoveries to large clinical trials, which in turn inform clinical practice. Figure 3-1 summarizes the complex interplay of relevant factors. If we assume that scientific discoveries are evaluated through proper clinical trials, clinical practice guidelines and performance indicators can be devised and con- tinuous evaluation through registries can measure improved outcomes as the system itself improves. In this context, there are at least a dozen major factors that must be iteratively improved in order for this system to work more efficiently and at lower cost (Califf et al., 2007). A specific model of this approach has been implemented by STS (Ferguson et al., 2000). Over time, STS has developed a clinical practice

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0 REDESIGNING THE CLINICAL EFFECTIVENESS RESEARCH PARADIGM Common Science and Technology Database Outcomes Education and Improved Feedback Outcomes Gaps Trials and Outcomes Projects FIGURE 3-2 The Society of Thoracic Surgeons evidence system model. Figure 3-2.eps SOURCE: Derived from Ferguson, T. B., et al. 2000. The STS national database: Current changes and challenges for the new millennium. Committee to establish a national database in cardiothoracic surgery, The Society of Thoracic Surgeons. The Annals of Thoracic Surgery 69(3):680-691. database that is used for quality reporting, and, increasingly, for continu- ously analyzing operative issues and techniques (Figure 3-2). The STS model also allows randomized trials to be conducted within the database. The most significant aspects of this model lie in its constantly evolving, continuously updated information base and its methods of engaging prac- titioners in this system by providing continuous education and feedback. Many have assumed that we must wait on fully functional electronic health records (EHRs) for such a system to work. However, we need not wait for some putatively ideal EHR to emerge. Current EHRs have serious short- comings from the perspective of clinical researchers, since these records must be optimized for individual provider–patient transactions. Conse- quently, they are significantly suboptimal with respect to coded data with common vocabulary—an essential feature for the kind of clinical research enterprise we envision. This deficit severely hobbles researchers seeking to evaluate aggregated patient information in order to draw inferential conclusions about treatment effects or quality of care. While we await the

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 TAKING ADVANTAGE OF NEW TOOLS AND TECHNIQUES Disease Registries—Granular, Detailed Integrated at “enterprise level” Primary Mental Cardio- Cancer Etc. vascular Care Health Electronic Health System A Health Records Health System B Adaptable Etc. to all FIGURE 3-3 Fundamental informatics infrastructure—matrix organizational structure. Figure 3-3.eps resolution of issues regarding EHR functionality, the best approach will be to construct a matrix between the EHR and continuous professional-based registries (disease registries) that measure clinical interactions in a much more refined and structured fashion (Figure 3-3). Such a system would allow us to perform five or six times as many trials as can now be done for the same amount of money; even better, such trials would be more relevant to clinical practice. As part of our Clinical and Translational Sci- ences Award (CTSA) cooperative agreement with the National Institutes of Health (NIH), we are presently working on such a county-wide matrix in Durham County, North Carolina (Michener et al., 2008). New Strategies for Incorporating Scientific Evidence into Clinical Practice New efficiencies can be gained through applying innovative informatics- based approaches to the broad pragmatic trials discussed above; however, we also must develop more creative methods of rapidly translating new scientific findings into early human studies. The basis for such POC clinical trials lies in applying an intervention to elucidate whether an intended bio- logical pathway is affected, while simultaneously monitoring for unantici- pated effects on unintended biological pathways (“off-target effects”). This process includes acquiring a preliminary indication of dose–response rela- tionships and of whether unintended pathways are also being perturbed (again, while providing a basic understanding of dose–response relation- ships). POC studies are performed to advance purely scientific understand-

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2 REDESIGNING THE CLINICAL EFFECTIVENESS RESEARCH PARADIGM ing or to inform a decision about whether to proceed to the next stage of clinical investigation. We used to limit ourselves by thinking that we could only perform POC studies in one institution at a time, but we now know that we can perform exactly the same trials, with the same standard operat- ing procedures and the same information systems in India and Singapore, as well as in North Carolina. The basis for this broadened capability, as in pragmatic clinical trials, is the building of clinical research networks that enable common protocols, data structures, and sharing of information across institutions. This broadening of scope affords the ability to rethink the scale, both physical and temporal, for POC clinical trials. The wide variation in costs in these different environments also deserves careful con- sideration by U.S. researchers. New Approaches to Old Problems: Conducting Pragmatic Clinical Trials When considering strategies for fostering innovation in clinical trials, several key points must be borne in mind. The most important is that there exists, particularly in the United States, an entrenched notion that each clinical trial, regardless of circumstances or aims, must be done under pre- cisely the same set of rules, usually codified in the form of standard oper- ating procedures (SOPs). Upon reflection, it is patently obvious that this is not (or should not be) the case; further, acting on this false assumption is impairing the overall efficiency of clinical trials. Instead, the conduct of trials should be tailored to the type of question asked by the trial, and to the circumstances of practice and patient enrollment for which the trial will best be able to answer that question. We need to cultivate environments where creative thought about the pragmatic implementation of clinical trials is encouraged and rewarded (“envelopes of innovation”), and given the existing barriers to changes in trial conduct, financial incentives may be required in order to encourage researchers and clinicians to “break the mold” of entrenched attitudes and practices. What is the definition of a high-quality clinical trial? It is one that provides a reliable answer to the question that the trial intended to answer. Seeking “perfection” in excess of this goal creates enormous costs while at the same time paradoxically reducing the actual quality of the trial by distracting research staff from their primary mission. Obviously, in the con- text of a trial evaluating a new molecular entity or device for the first time in humans, there are compelling reasons to measure as much as possible about the subjects and their response to the intervention, account for all details, and ensure that the intensity of data collection is at a very high level. Pragmatic clinical trials, however, require focused data collection in large numbers of subjects; they also take place in the clinical setting where their usual medical interactions are occurring, thereby limiting the scope of detail

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 TAKING ADVANTAGE OF NEW TOOLS AND TECHNIQUES for the data that can be collected on each subject. To cite a modified Institute of Medicine definition of quality, “high quality with regard to procedural, recording and analytic errors is reached when the conclusion is no different than if all of these elements had been without error” (Davis, 1999). Efficacy trials are designed to determine whether a technology (a drug, device, biologic, well-defined behavioral intervention, or decision support algorithm) has a beneficial effect in a specific clinical context. Such inves- tigation requires carefully controlled entry criteria and precise protocols for intervention. Comparisons are often made with a placebo or a less relevant comparator (these types of studies are not sufficiently informative for clinical decision making because they do not measure the balance of risk and benefit over a clinically relevant period of time). Efficacy trials— which speak to the fundamental question, “can the treatment work?”—still require a relatively high level of rigor, because they are intended to establish the effect of an intervention on a specific end-point in a carefully selected population. In contrast, pragmatic clinical trials determine the balance of risk and benefit in “real world” practice; i.e., “Should this intervention be used in practice compared with relevant alternatives?” (Tunis et al., 2003). The population of such a study is allowed to be “messy” in order to simulate the actual conditions of clinical practice; operational procedures for the trial are designed with these decisions in mind. The comparator is pertinent to choices that patients, doctors, and health systems will face, and outcomes typically are death, clinical events, or quality of life. Relative cost is impor- tant and the duration of follow-up must be relevant to the duration that will be recommended for the intervention in practice. When considering pragmatic clinical trials, I would argue we actually do not want professional clinical trialists or outstanding practitioners in the field to dominate our pool of investigators. Rather, we want to incorporate real-world conditions by recruiting typical practitioners who practice the way they usually do, with an element of randomization added to the system to provide, at minimum, an inception time and a decision point from which to begin the comparison. A series of papers recently have been published that present a detailed summary of the principles of pragmatic clinical trials (Armitage et al., 2008; Baigent et al., 2008; Cook et al., 2008; Duley et al., 2008; Eisenstein et al., 2008; Granger et al., 2008; Yusuf et al., 2008). The Importance of Finding Balance in Assessing Data Quality If we examine the quality of clinical trials from an evidence-based perspective we might emerge with a very different system (Yusuf, 2004). We know, for example, that an on-site monitor almost never detects fraud, largely because if someone is clever enough to think they can get away with

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 REDESIGNING THE CLINICAL EFFECTIVENESS RESEARCH PARADIGM TABLE 3-1 Taxonomy of Clinical Errors Error Type Monitoring Method Design error Peer review, regulatory review, trial committee oversight Procedural error Training and mentoring during site visits; simulation technology Recording error Random Central statistical monitoring; focused site monitoring based on performance metrics Fraud Central statistical monitoring; focused site monitoring based on unusual data patterns Analytical error Peer review, trial committees, independent analysis fraud, that person is likely to be adroit at hiding the signs of their deception from inspectors. A better way to detect fraud is through statistical process control, performed from a central location. For example, a common indica- tor of fraudulent data is that the data appear to be “too perfect.” If data appear ideal in a clinical trial, they are unlikely to be valid: That is not the way that human beings behave. Table 3-1 summarizes monitoring methods to find error in clinical trials that take advantage of a complete perspective on the design, conduct, and analysis of trials. Recent work sheds light on how to take advantage of natural units of practice (Mazor et al., 2007). It makes sense, for example, to randomize clusters of practices rather than individuals when a policy is being evalu- ated (versus treating an individual). Several studies that have followed this approach were conducted as embedded experiments within ongoing regis- tries; the capacity to feed information back immediately within the registry resulted in improvements in practice. Although the system is not perfect, there is no question that it makes possible the rapid improvement of prac- tice and allows us to perform trials and answer questions with randomiza- tion in that setting. Disruptive Technologies and Resistance to Change All this, however, suggests the question: If we are identifying more efficient ways to do clinical trials, why are they not being implemented? The problem is embedded in the issue of disruptive technology—initiating a new way of doing a clinical trial is disruptive to the old way. Such disruption upsets an industry that has become oriented, both financially and philosophically, toward doing things in the accustomed manner. In less highly regulated areas of society, technologies develop in parallel and the “winners” are chosen by the marketplace. Such economic Darwinian

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 TAKING ADVANTAGE OF NEW TOOLS AND TECHNIQUES selection causes companies that remain wedded to old methods to go out of business when their market is captured by an innovator who offers a disruptive technology that works better. In most markets, technology and organizational innovation drive cost and quality improvement. Providing protection for innovation that will allow those factors to play out natu- rally in the context of medical research might lead to improved research practices, thereby generating more high-quality evidence and, eventually, improving outcomes. In our strictly regulated industry, however, regulators bear the mantle of authority, and the risk that applying new methods will result in lower quality is not easily tolerated. This in turn creates a decided barrier to innovation, given the extraordinarily high stakes. There is a question that is always raised in such discussions: If you do human trials less expen- sively and more efficiently, can you prove that you are not hurting patient safety? What effect is all of this having? A major impact is cost: Many recent cardiovascular clinical outcomes trials have cost more than $350 million dollars to perform. In large part this expense reflects procedures and proto- cols that are essentially unnecessary and unproductive, but required none- theless according to the prevailing interpretation of regulations governing clinical trials by the pharmaceutical and device companies and the global regulatory community. Costing out the consequences of the current regulatory regime can yield staggering results. As one small example, a drug already on the mar- ket evidenced a side effect that is commonly seen in the disease for which it is prescribed. The manufacturer believed that it was required to ship by overnight express the adverse event report to all 2,000 investigators, with instructions that the investigators review it carefully, classify it, and send it to their respective IRBs for further review and classification. The cost of that exercise for a single event that contributed no new knowledge about the risk and benefit balance of the drug was estimated at $450,000. Starting a trial in the United States can cost $14,000 per site before the first patient is enrolled simply because of current regulations and pro- cedures governing trial initiation, including IRB evaluation and contract- ing. A Cooperative Study Group funded by the National Cancer Institute recently published an analysis demonstrating that a minimum of more than 481 discrete processing steps are required for an average Phase II or Phase III cancer protocol to be developed and shepherded through various approval processes (Dilts et al., 2008). This results in a delay of more than 2 years from the time a protocol is developed until patient enrollment can begin, and means that “the steps required to develop and activate a clini- cal trial may require as much or more time than the actual completion of a trial.”

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