Redesigning the Clinical Effectiveness Research Paradigm Innovation and Practice-Based Approaches: Workshop Summary (2010) / Chapter Skim
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3 Taking Advantage of New Tools and Techniques
3 Taking Advantage of New Tools and Techniques
Pages 155-220
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From page 155... ...
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.
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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.
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She proposes opportunities to better capture and use these data to understand clinical effectiveness. INNOVATIVE APPROACHES TO CLINICAL TRIALS Robert M
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Since randomization 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.
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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 cardiovascular medicine, where there is a long history of applying scientific discoveries to large clinical trials, which in turn inform clinical practice.
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The most significant aspects of this model lie in its constantly evolving, continuously updated information base and its methods of engaging practitioners in this system by providing continuous education and feedback. Many have assumed that we must wait on fully functional electronic health records (EHRs)
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. New Strategies for Incorporating Scientific Evidence into Clinical Practice New efficiencies can be gained through applying innovative informaticsbased 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.
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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.
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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.
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For example, a common indicator 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.
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In large part this expense reflects procedures and protocols that are essentially unnecessary and unproductive, but required nonetheless 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.
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. A collaboration among the FDA, industry, academia, patient advocates, and nonacademic clinical researchers, CTTI is designed to conduct empirical studies that will provide evidence to support redesign of the overall framework of clinical trials and to eliminate practices that increase costs but provide no additional value.
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Harvard Medical School BWH DEcIDE Research Center on Comparative Effectiveness Research Instrumental Variable Analyses for Comparative Effectiveness Research Using Clinical and Administrative Databases Physicians and insurers need to weigh the effectiveness of new drugs against existing therapeutics in routine care to make decisions about treatment and formularies. Because FDA approval of most new drugs requires demonstrating efficacy and safety against placebo, there is limited interest by manufacturers in conducting such head-to-head trials.
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We have a large continuum of comparative effectiveness research, within which some questions are heavily confounded by design while others are not; the separation is usually by unintended treatment effects and intended treatment effects. An example is in the use of selective Cox-2 inhibitors (coxibs)
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This is necessary for instrumental variables to produce valid results. Consequently, in working with such instruments, researchers have to identify a sort of quasi-random treatment assignment in the real world.
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Strong Treatment Preference Several papers have contributed to our understanding of this valuable instrument for evaluating the comparative effectiveness of therapeutics, which considers such instruments as the distance to specialist, geographic area, physician prescribing preference, and hospital formularies (Brookhart et al., 2006; McClellan et al., 1994; Stukel et al., 2007)
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This is an example where the confounding is strong and the confounding factor is either not measured in claims data or is measured only to a small extent. Let us consider three core assumptions about instrumental variables (Angrist, 1996)
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Looking at the effect estimates in Figure 3-7 we find that the protective effect of heart catheterization in patients with acute myocardial infarction Decrease in ef fect size with better adjustment for measured and unmeasured confounders: RD FIGURE 3-7 Regional variation in cardiac catheterization and risk of death. SOURCE: Journal of the American Medical-Association 297(3)
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Comparative effectiveness research should routinely explore whether
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We have found that the physician prescribing preference instrument is worth considering in many situations of drug effectiveness research. We have further recommended that instrumental variable analyses should be secondary to conventional regression modeling until we better understand the qualities of preference-based instruments and how to best empirically test IV assumptions.
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The other improvement is incorporating different sources of information to enable better conclusions about comparative effectiveness. Both use the Bayesian approach to statistics (Berry, 1996, 2006)
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probability of the null hypothesis. The Bayesian approach has a characteristic that is very important in designing clinical studies: It enables calculating probabilities of future observations based on previous observations.
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Late-phase clinical trials tend to be large. Large clinical trials are expensive, which increases the cost of health care.
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Protocols may be different. Some sources may be clinical trials while others are databases accumulated in clinical practice.
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For example, for the survival benefit of tamoxifen for women with hormone-receptor positive tumors we based the prior distribution on the Oxford Overview of randomized trials, but with much greater standard deviation than that from the Overview to account for the possibility that tamoxifen used in clinical practice might not have the same benefit as in clinical trials. We generated many thousands of cohorts of 2 million U.S.
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However, RCTs have weaknesses and limitations, including problems with generalizability, duration, and costs. Physiology-based models, such as the Archimedes model, have the potential to augment and enhance knowledge gained from clinical trials and can be
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Multiple uses of the Archimedes model to enhance and extend existing clinical trials as well as to conduct virtual comparative effectiveness trials also will be discussed. Strengths and Weakness of Randomized Controlled Trials The main strength of randomized controlled trials is that the random assignment to treatment and control group renders those groups equivalent and eliminates bias by indication, resulting in intervention and control groups that are balanced in known and unknown parameters.
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The virtual individual has a virtual liver that produces virtual glucose, a virtual gut that absorbs virtual nutrients, a virtual pancreas with virtual beta cells that make virtual insulin, and virtual muscle mass and virtual fat cell mass that utilizes glucose as a function of the amount of virtual insulin available. Figure 3-9 shows a small portion of the model, but illustrates the types of variables and relationships that are in the Archimedes model.
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SOURCE: Copyright © 2003 American Diabetes Association. From Diabetes Care, Vol.
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The Archimedes model can replicate that process by enrolling virtual people with the exact characteristics of their counterparts in real clinical trials and randomly assign them to virtual treatments that represent the real treatments used in the trial, record virtual outcomes using the same definitions and methods used in the trials, and then compare the results of the virtual trial to those of the real trial. Data available from separate Phase I or Phase II trials can be used to estimate the effects of the intervention on the relevant biomarkers.
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The results for 18 clinical trials have been published. Figure 3-12 compares the results of 74 simulated trials in diabetes, lipid control, and cardiovascular disease, and graphs the actual relative risk found from a trial and the results calculated by the Archimedes model.
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Another is to facilitate the design of new trials. For example, as the validations described above have shown the Archimedes model can be used to estimate the rates of outcomes in control groups and the expected magnitude of the effects of treatments.
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2 0.1 0 0 0.1 0. 2 0.3 0.4 0.5 0.6 Actual Ef fect Size from Trials FIGURE 3-12 Comparison of Archimedes model and multiple trials.
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The volume and quality of data of these types can be expected to increase as the use of electronic medical records spreads. The key to all of these applications is that if a model is to be used to predict, plan, extend, or help fill the gaps between clinical trials, it must prove its ability to reproduce and predict the results of many real clinical trials, using only data available at the start of the trial, and not using any results from the trial to build or modify the model to fit the results of new trials.
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Several of these findings have sufficient supporting evidence for functional significance or biologic plausibility, and many are sufficiently common that they provide real potential for translation into diagnostic, preventive, or therapeutic interventions. In this new era of genomic discovery, one of the most pressing questions for clinical effectiveness research is thus: What is needed to facilitate the reliable and timely introduction of emerging genetic information into research and clini cal databases?
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approach has been enormously successful in identifying genetic variants related to complex diseases, or diseases likely influenced by multiple genes and environmental factors. The first notable success of this method came in March 2005, with the identification of a variant in the gene for complement factor H (CFH)
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Some, such as the strong associations of prostate cancer with SNPs in the 8q24 region (Scott et al., 2007) , and Crohn's disease with the 5p13 region (Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls, 2007)
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. In addition, application of GWA genotyping to long-standing, extensively characterized cohorts such as the Framingham Heart Study and Women's Health Study (Cupples et al., 2007; Ridker et al., 2008)
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, but the potential for conducting this research in large healthcare systems involving hundreds of thousands or millions of participants should not be overlooked. Use of GWA Information in Research and Clinical Databases One way of using this emerging genomic technology in research and clinical databases is to perform GWA genotyping in patients with comprehensive (and typically, electronic)
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Genomic Information Suitable for Clinical Effectiveness Research Assays of genetic variants related to two traits -- Type 2 diabetes risk and warfarin dosing requirements -- have sufficient scientific foundation and clinical availability to serve as prototypes for applying genomic information emerging from GWA studies to clinical effectiveness research. In a longer paper we also might have considered CFH and age-related macular degeneration (Klein et al., 2005)
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. deCODE Diagnostics also offers DNA-based tests for assessing risk of atrial fibrillation, myocardial infarction, glaucoma, and prostate cancer, all conditions for which deCODE Genetics published the first or one of the first GWA studies (Gudbjartsson et al., 2007; Gudmundsson et al., 2007; Helgadottir et al., 2007; Thorleifsson et al., 2007)
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. Marketing or application of diagnostic genetic testing in this way has raised some anxieties, primarily due to the lack of evidence that genetic testing improves outcome or adds significantly to readily available clinical information (Haga et al., 2003; Janssens et al., 2006)
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. Incorporating Genomic Information into Clinical Effectiveness Research With the examples of TCFL2 and warfarin-dosing-related variants in hand, we can return to our original question of what is needed to facilitate the reliable and timely introduction of emerging genomic information into
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Epidemiologic Information Needed Much of the basic information needed for informed decision making about newly identified genetic variants relates to fundamental epidemiologic questions such as prevalence, risk, and potential for risk reduction. Genetic variants such as those in TCFL2, CYP2C9, and VKORC are essentially risk factors for complex diseases, similar in many ways to nongenetic risk factors such as obesity, smoking, or hypertension.
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BOX 3-1 Epidemiologic Information Needed to Assess the Usefulness of Genetic Variants in Clinical Practice • Prevalence • Magnitude of increased risk associated with variant • Consistency of increased risk across multiple groups defined by age, sex, race/ethnicity, exposures • Independence of associated risk from other known risk factors • Association of variant with earlier onset or more severe disease course • Association of variant with response to treatment (gene–environment interaction)
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This would include variants that were not assayed on the original genotyping platform or, possibly, not even yet known to exist. Substantial investigative effort was needed to narrow this down; in this instance, the deCODE investigators identified another SNP, rs7903146, that carried a higher relative risk at a BOX 3-2 Genetic Information Needed to Select Genetic Variants for Use in Clinical Effectiveness Research • Location and frequency of variants in and near association region • Allelic forms including insertions, deletions, and duplications, as well as single nucleotide polymorphisms • Linkage-disequilibrium relationships among these variants • Type of variants: coding, promoter, splice site • Ease of typing and reliability of assay for each variant
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SOURCE: Reprinted by permission from Macmillan Publishers, Ltd. Nature Genetics 38(3)
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It also would need to meet estab BOX 3-3 Laboratory and Clinical Infrastructure Needed to Conduct Clinical Effectiveness Research on Genetic Variants • Valid FDA-approved test • Insurer-approved reimbursement • CLIA-certified laboratory • Available/affordable testing • Electronic health records • Confidentiality/privacy protections • Large-scale databases for sharing of research data with qualified investigators
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Visitors to dbGaP can view phenotype summary data, genotype summary data, and pre-computed or published genetic associations. As such it provides a powerful tool for identifying emerging genomic information that may potentially be applied to clinical effectiveness research, but as yet the association
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Clinical effectiveness research on genomic information will be difficult, if not impossible, to conduct on a large scale without the formal legal protection against discrimination provided by the Genetic Information Non-Discrimination Act (GINA)
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Flexible approaches to genetic counseling also are needed, including approaches for providing adequate counseling, where appropriate, through means other than one-on-one counseling with a certified genetic counselor. There are simply not enough certified genetic counselors available, nor is that level of counseling necessary for variants of modest effect, to provide it for every genetic test performed in the course of clinical care or effectiveness research.
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The agenda for clinical effectiveness research on emerging genomic information is clearly substantial and will likely continue for some time after initial identification of a clinically important variant. Conclusion Advances in genotyping technology coupled with expanding knowledge of genome structure and function have fueled a virtual explosion of genomic information on common, complex diseases.
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breast cancer mortality: A Bayesian approach. Journal of the National Cancer Institute Monographs (36)
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2007. Evaluating the validity of an instrumental variable study of nuero leptics: Can between-physician differences in prescribing patterns be used to estimate treatment effects?
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2007. The Framingham Heart Study 100k SNP genome-wide association study resource: Overview of 17 phenotype working group reports.
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2007. Genome-wide association study identifies novel breast cancer susceptibility loci.
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2007. Genome-wide association studies provide new insights into type 2 dia betes aetiology.
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2006. What is the clinical utility of genetic testing?
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2007. A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer.
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2001. Repli cation validity of genetic association studies.
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2009. Instrumental variables II: Instrumental variable application -- in 25 variations, the physician prescribing preference generally was strong and reduced covariate imbalance.
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2006. Simultane ous assessment of short-term gastrointestinal benefits and cardiovascular risks of selective cyclooxygenase 2 inhibitors and nonselective nonsteroidal antiinflammatory drugs: An instrumental variable analysis.
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2004. Bayesian Approaches to Clinical Trials and Health-care Evaluation.
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2007. Genome-wide association study of prostate cancer identifies a second risk locus at 8q24.
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2007. Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes.
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