5

The Evidence Base for High-Quality Cancer Care

“Decisions about the care of individual patients should be based on the conscientious, explicit, and judicious use of current best evidence” (IOM, 2008b, p. 2). The committee’s conceptual framework (see Figure S-2) depicts the evidence base as supporting patient-clinician interactions, because a high-quality cancer care delivery system uses results from scientific research, such as clinical trials and comparative effectiveness research (CER), to inform medical decisions. A high-quality cancer care delivery system depends upon clinical research that gathers evidence of the benefits and harms of various treatment options so that patients, in consultation with their clinicians, can make treatment decisions that are consistent with their needs, values, and preferences.

The relative weight that patients place on each consideration related to their diagnosis and treatment tends to vary across different populations. Older adults faced with a cancer diagnosis, for example, may value outcomes different from the ones younger patients value, and may be more apt to choose treatment options that will maintain quality of life for as long as possible rather than focusing solely on increasing the length of survival or disease remission as measured by biomarkers (see discussion in Chapter 2 on the unique needs of older adults with cancer). The recent emphasis on molecularly targeted medicine in clinical cancer research could greatly improve the quality of cancer care by enabling physicians to effectively target therapeutic interventions to the patients for whom they are most suited and to avoid treating patients for whom the interventions will not be effective and may be unsafe (see discussion in Chapter 2 on



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5 The Evidence Base for High-Quality Cancer Care “D ecisions about the care of individual patients should be based on the conscientious, explicit, and judicious use of current best evidence” (IOM, 2008b, p. 2). The committee’s concep- tual framework (see Figure S-2) depicts the evidence base as supporting patient-clinician interactions, because a high-quality cancer care delivery system uses results from scientific research, such as clinical trials and comparative effectiveness research (CER), to inform medical decisions. A high-quality cancer care delivery system depends upon clinical re- search that gathers evidence of the benefits and harms of various treat- ment options so that patients, in consultation with their clinicians, can make treatment decisions that are consistent with their needs, values, and preferences. The relative weight that patients place on each consideration related to their diagnosis and treatment tends to vary across different popula- tions. Older adults faced with a cancer diagnosis, for example, may value outcomes different from the ones younger patients value, and may be more apt to choose treatment options that will maintain quality of life for as long as possible rather than focusing solely on increasing the length of survival or disease remission as measured by biomarkers (see discussion in Chapter 2 on the unique needs of older adults with cancer). The recent emphasis on molecularly targeted medicine in clinical cancer research could greatly improve the quality of cancer care by enabling physicians to effectively target therapeutic interventions to the patients for whom they are most suited and to avoid treating patients for whom the interventions will not be effective and may be unsafe (see discussion in Chapter 2 on 207

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208 DELIVERING HIGH-QUALITY CANCER CARE trends in cancer research and practice changes). The focus on improving the evidence base for cancer is consistent with the Institute of Medicine’s (IOM’s) 1999 report Ensuring Quality Cancer Care, which recommended investing in clinical trials to address questions about cancer care manage- ment and health services research to understand care patterns associated with good health outcomes (IOM and NRC, 1999). A recent IOM report concluded that “despite the accelerating pace of scientific discovery, the current clinical research enterprise does not suffi- ciently address pressing clinical questions. The result is decisions by both patients and clinicians that are inadequately informed by the evidence” (IOM, 2012a, p. 20). For example, Villas Boas and colleagues (2012) and El Dib and colleagues (2007) found that about half of Cochrane systematic reviews had sufficient evidence to inform clinical practice. Oftentimes, research participants are not representative of the popu- lation that actually contracts the disease; older adults, individuals with comorbidities, members of racial and ethnic minorities, and people who live in rural areas are consistently underrepresented in clinical research (EDICT, 2008). Investigators also often fail to collect data that could be used to draw conclusions about factors that influence the course of the dis- ease and provide information about the patient experience with care (e.g., quality of life, functional and cognitive status, symptoms, socioeconomic status, literacy, numeracy, language, culture, education, transportation, social supports, neighborhood, behavioral health, housing, family capac- ity, comorbidity, and psychological state) (Ganz, 2012). Although health information technology (IT) has great promise for improving research and clinical knowledge to guide decisions, there need to be advances in health IT infrastructure, computational capabilities, and research methods to fulfill this potential (IOM, 2012a). The complexity of cancer and the diverse treatment options available exacerbate the challenges of developing an evidence base that will ad- equately support clinical decision making. There are hundreds of different types of cancer, with multiple stages of disease (e.g., precancer, early-stage disease, metastatic disease). The multiple treatment modalities and com- bination strategies for cancer treatment necessitate coordinated teams of professionals with multiple skill sets. Additionally, the toxicity of many treatment options often requires patients and clinicians to make difficult decisions that weigh the benefits and harms of alternative treatment ap- proaches. Although cancer care is evolving quickly, with manufacturers marketing new drugs and devices that have the potential to improve current treatment, those innovations come with substantial human and financial costs. This chapter summarizes how the evidence base for decision mak- ing in cancer care is generated and discusses the need to improve the

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THE EVIDENCE BASE FOR HIGH-QUALITY CANCER CARE 209 breadth and depth of information collected in clinical cancer research, as well as the potential to improve the use of technology to collect, organize, and analyze data from various sources. The chapter focuses on clinical research with the potential to generate evidence that could directly in- form medical decision making; a discussion of basic research is outside the scope of this report. Other topics relevant to delivering evidence- based cancer care are discussed elsewhere in this report. New models of care delivery are discussed in Chapter 8 and performance improvement initiatives are discussed in Chapter 7. This chapter builds on the IOM’s previous consensus studies on cancer clinical trials, CER, and a learning health care system (IOM, 2008a,b, 2009a,b, 2010a,b, 2012a,b). The com- mittee identifies two recommendations to improve the evidence base for high-quality cancer care. How the Evidence Base for Cancer Care Decisions Is Generated Both publicly and privately funded research will be necessary to improve the evidence base for cancer care. For-profit industries generally fund research focused on developing new drugs and devices for treating cancer, while public funders often support research addressing “questions that are important to patients but are less likely to be top priorities of industry” (IOM, 2010b, p. 1). This section addresses trials of new drugs, biologics, and devices, as well as CER. Trials of New Drugs, Biologics, and Devices Manufacturers of drugs, biologics, and devices leverage scientific advances to bring new treatments to the market with the potential to im- prove patient outcomes. The Food and Drug Administration (FDA), the federal agency charged with regulating pharmaceuticals and medical de- vices, requires manufacturers to submit scientific evidence that establishes the safety and effectiveness of their products prior to making them avail- able to the public (FDA, 2012a,b). The FDA approval or clearance allows the marketing of new drugs, biologics, and devices with the potential to improve outcomes for patients with cancer, although some experts have raised concern that the FDA’s medical device approval/clearance pro- cesses are less rigorous than for drugs (IOM, 2013; Meropol et al., 2009). An IOM committee reviewing the process by which most medical devices enter the market concluded that the process often fails to adequately en- sure safety and effectiveness (IOM, 2011b). The IOM recommended that the FDA design a new medical-device regulatory framework. Neverthe-

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210 DELIVERING HIGH-QUALITY CANCER CARE less, clinical trials conducted by manufacturers can provide important information for clinical decision making. Research conducted by manufacturers tends to be narrowly focused on allowing the manufacturers to market efficacious products that may improve patient care, influence package inserts or labeling claims on their products, or expand market share. As a result, such research often fails to address many additional research questions relevant to clinical care. An IOM report on cancer clinical trials noted that companies often lack incentives to conduct clinical trials that compare the effectiveness of dif- ferent treatment options already approved for clinical use; combine novel treatments developed by different sponsors; determine optimal duration and dose of drugs in clinical use; or test multimodality treatments, such as radiation therapy, surgery, or devices in combination with drugs (IOM, 2010b). In addition, manufacturers often conduct their research with highly selective patient populations and through carefully defined and moni- tored treatment regimens, with the goal of providing safety and efficacy data to the FDA. The data collected by manufacturers may therefore not be generalizable to real-world clinical practice. Certain populations are routinely understudied due to strict eligibility criteria, including older adults and patients with multiple chronic conditions, and outcomes (such as the impact of treatment on physical or cognitive function) that are important to patients and their caregivers are often unmeasured. Manu- facturers are also unlikely to study certain types of treatments that do not require regulatory approval, such as surgery and radiation therapy. Comparative Effectiveness Research Because of the narrow focus of research conducted for regulatory approval, there are often many remaining practical questions when a drug or device is introduced into the market, which go beyond those typically addressed by regulatory agencies. There has been recent inter- est in using CER to fill these knowledge gaps (IOM, 2009a, 2011a; Lyman and Levine, 2012; PCORI, 2012a; Ramsey et al., 2013). CER is defined as “the generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition or to improve the delivery of care. The purpose of CER is to assist consumers, clinicians, purchasers, and policy makers to make informed decisions that will improve health care at both the individual and population levels” (IOM, 2009a, p. 13). Research that is compatible with the aims of CER has six defining characteristics:

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THE EVIDENCE BASE FOR HIGH-QUALITY CANCER CARE 211 1. The objective is to inform a specific clinical question. 2. It compares at least two alternative interventions, each with the potential to be a “best practice.” 3. It addresses and describes patient outcomes at both a population and a subgroup level. 4. It measures outcomes that are important to patients, including harms and benefits. 5. It uses research methods and data sources that are appropriate for the question of interest. 6. It is conducted in settings as close as possible to the settings in which the intervention will be used. CER can be conducted using multiple research methodologies, including clinical trials as well as observational research and systematic reviews (see Box 5-1). The appropriate methodology depends on the type of question the research is intended to answer. The American Recovery and Reinvestment Act of 20091 appropriated $1.1 billion for CER, and the IOM was charged with identifying an initial set of CER priorities (IOM, 2009a). These priorities included six topics in cancer, including screening technologies for colorectal and breast cancer; management strategies for localized prostate cancer; imaging technolo- gies for diagnosis, staging, and monitoring of all cancers; use of bio- marker analysis in risk assessment and treatment strategies for common cancers; and comparing treatment strategies for liver metastases. The Patient Protection and Affordable Care Act of 20102 (ACA) rein- forced the importance of CER and created the Patient-Centered Outcomes Research Institute (PCORI), a new institute responsible for establishing and implementing a research agenda that provides “information about the best available evidence to help patients and their health care providers make more informed decisions” (PCORI, 2012a). The institute has a trust fund of $150 million in annual appropriations, plus an annual per-capita charge for each enrollee from insurance plans through 2019 (Clancy and Collins, 2010). The National Cancer Institute’s (NCI’s) Clinical Trials Cooperative Group Program is one of the major funders of CER in cancer. Many of the Cooperative Groups’ studies have generated data that have informed clinical decision making and set the standard of care in cancer. Their stud- ies regularly compare alternative interventions, describe results at the 1  The American Recovery and Reinvestment Act of 2009, Public Law 111-5, 111th Congress, 1st Sess. (February 17, 2009). 2  Patient Protection and Affordable Care Act, Public Law 111-148, 111th Congress, 2nd Sess. (March 23, 2010).

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212 DELIVERING HIGH-QUALITY CANCER CARE BOX 5-1 Types of Comparative Effectiveness Research Studies Experimental study: A study in which the investigators actively intervene to test a hypothesis. •  ontrolled trials are experimental studies in which a group receives the C intervention of interest while one or more comparison groups receive an active comparator, a placebo, no intervention, or the standard of care, and the outcomes are compared. In head-to-head trials, two active treatments are compared. • n a randomized controlled trial (RCT), participants are randomly al- I located to the experimental group or the comparison group. Cluster ran- domized trials are RCTs in which participants are randomly assigned to the intervention or comparison in groups (clusters) defined by a common feature, such as the same physician or health plan. Observational study: A study in which investigators simply observe the course of events. • n prospective observational studies, the exposure of interest is studied I using data stored in registries, which can require years to accumulate the needed numbers of patients and outcomes. • n cohort studies, groups with certain characteristics or receiving certain I interventions (e.g., premenopausal woman receiving chemotherapy for breast cancer) are monitored over time to observe an outcome of interest (e.g., loss of fertility). • n case-control studies, groups with and without an event or outcome I are examined to see whether a past exposure or characteristic is more prevalent in one group than in the other. • n cross-sectional studies, the prevalence of an exposure of interest is I associated with a condition (e.g., prevalence of hysterectomy in African American versus white women) and is measured at a specific time or time period. Systematic review (SR): A scientific investigation that focuses on a specific ques- tion and that uses explicit, planned scientific methods to identify, select, assess, and summarize the findings of similar but separate studies. It may or may not in- clude a quantitative synthesis (meta-analysis) of the results from separate studies. •  meta-analysis is an SR that uses statistical methods to combine quanti- A tatively the results of similar studies in an attempt to allow inferences to be made from the sample of studies and applied to the population of interest. SOURCES: IOM, 2011a. Adapted from Last, 1995.

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THE EVIDENCE BASE FOR HIGH-QUALITY CANCER CARE 213 population and subpopulation levels, and measure benefits and risks that are important to patients. The Cooperative Groups’ inclusion of the Com- munity Clinical Oncology Program means that many trials are conducted by community practices, where the majority of cancer patients are treated, representing a more generalizable population. The Cooperative Groups’ research regularly addresses interventions not studied in FDA registration trials, such as surgical innovations and in-depth evaluations of imaging and medical devices (Hahn and Schilsky, 2012; Schilsky, 2013). Despite progress, the NCI convened the IOM to provide advice on im- provements and reorganization in the Cooperative Groups’ research that could help them reach their full potential and conduct timely, large-scale, and innovative clinical trials needed to improve patient care (IOM, 2010b; NCI, 2012c). The IOM released its recommendations in 2010 and the Co- operative Groups are currently reorganizing within a National Clinical Trials Network (NCTN). Given current financial constraints, the NCI is still grappling with how to prioritize new research and create a balanced portfolio of clinical trials on new cancer treatments, CER, and correlative biomarker research (NCI, 2012b). Thus, there is some uncertainty about the types and focus of research that the NCTN will conduct in the future. The Agency for Healthcare Research and Quality (AHRQ) Effective Health Care Program is the federal government’s major funder of CER. This program includes several initiatives focused on CER: (1) Evidence- Based Practice Centers—which conduct systematic reviews of the lit- erature and are involved in developing the methodology of systemic reviews; (2) Developing Evidence to Inform Decisions about Effectiveness Centers—which are involved in developing new CER evidence; (3) The Centers for Education and Research on Therapeutics—which conduct research and provide education to advance the optimal use of drugs, de- vices, and biological products; and (4) the John M. Eisenberg Clinical De- cisions and Communications Science Center—which translates evidence into lay language (AHRQ, 2013a). AHRQ’s Effective Health Care Program has completed more than 50 research summaries, systematic reviews, and reports on cancer, as well as other topics relevant to cancer care (e.g., patient-centeredness, end-of-life issues) (AHRQ, 2013b). Improving the Breadth of Information Collected For clinical research to improve the quality of cancer care, research- ers need to study populations that are representative of clinical practice. Participation in a clinical trial can be a valid treatment option for many individuals with cancer, especially for individuals who have exhausted the standard of care options. The ACA acknowledges the importance of

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214 DELIVERING HIGH-QUALITY CANCER CARE participation in clinical trials and requires insurers to cover research par- ticipants’ routine care costs during approved trials (IOM, 2010b). Currently, however, only 3 percent of adults with cancer participate in clinical trials (IOM, 2010b). Members of racial and ethnic minorities, individuals with comorbidities, older adults, low-income individuals, and people who live in rural areas are consistently underrepresented in cancer research (EDICT, 2008; IOM, 2010b). And although the major- ity of cancer patients are treated in community settings, the majority of cancer patients who enroll in clinical trials are treated at academic cancer centers (Cox and McGarry, 2003; IOM, 2010b; Somkin et al., 2005). As cancer treatment moves toward more molecularly targeted therapies, the underrepresentation of certain population segments becomes particularly problematic; this type of research requires large numbers of patients will- ing to participate in trials. The committee is particularly concerned about the lack of clinical trial research focused on older adults, given its statement of task. Research shows that not only are older adults often excluded from trials, but when they are included they are not representative of the typical older adult; they are younger and healthier than average (Cerreta et al., 2012; Dhruva and Redberg, 2008; Van Spall et al., 2007). As mentioned in Chapter 2, there are many unique considerations to treating older adults with cancer. Older adults with cancer may have different treatment goals from those of younger patients (e.g., quality of life vs. length of life), often respond differently to treatment than do younger patients, and are more sensitive to toxicity and side effects. They are also more likely to have comorbidities that may influence the effects of treatment on their health. At the same time, older adults are often some of the first individuals using a newly available drug because the majority of cancer patients are over 65 years. When older adults and individuals with comorbidities are underrepresented in cancer clinical trials, clinicians are forced to extrapo- late from clinical trials conducted on younger, healthier adults and apply that information to older adults, hoping that the information will be rel- evant in the older population. Although federal agencies have mandated the recruitment of women and minorities to oncology trials to address those groups’ past exclusion, policies on the inclusion of older adults are less stringent or nonexistent (FDA, 1998; NIH, 2001).3 The inclusion of older adults in clinical research is complicated by 3  Under the Food and Drug Administration Modernization Act of 1998 Sec. 115. (b) Women and Minorities. Section 505(b)(1) 21 U.S.C. 355(b)(1) was amended by adding the following: “The Secretary shall, in consultation with the Director of the National Institutes of Health and with representatives of the drug manufacturing industry, review and develop guidance, as appropriate, on the inclusion of women and minorities in clinical trials.”

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THE EVIDENCE BASE FOR HIGH-QUALITY CANCER CARE 215 the fact that chronological age is an inadequate method of characterizing individuals. Many individuals qualify as older adults based on their chronologic age, but are functionally much younger, and the opposite can also be true (see discussion in Chapter 2). As a result, even when eligibil- ity criteria are set to match the population with the disease, clinicians and ethics boards often prevent frail individuals from participating in trials (Cerreta et al., 2012). Some researchers have suggested that “clinical trials designed with physiological age in mind would certainly lead to more meaningful results” (Herrera et al., 2010, p. S106). There are many barriers to older adults’ participation in clinical re- search. Trials often have stringent eligibility criteria with regard to comor- bidities, concomitant medications, and medical histories. In an evaluation of older adults participating in NCI-sponsored clinical trials, Lewis and colleagues (2003) found that the majority of trials excluded participation if a person had hematologic, hepatic, renal, or cardiac abnormalities, all of which are common in older adults (see discussion in Chapter 2). Approxi- mately 80 percent of the trials also required participants to be ambulatory and capable of caring for themselves (Lewis et al., 2003). Because many older adults do not drive, transportation and the cost of traveling to the research location can also be challenging. In addition, the attitudes of both clinicians and patients can impede their participation. A study by Javid and colleagues (2012) found that family-related and personal concerns played a greater role in older adults’ decisions not to participate in a clinical trial than in younger cancer patients’ decisions. Patients who were older were also less likely than younger patients to believe their participation in a clinical trial would benefit future generations and more likely to believe that participation in a clinical trial would be burdensome. Clinicians have few incentives to offer patients enrollment in clinical trials, and regularly cite concerns about drug toxicity and the impact of treatment as reasons to not enroll older adults (Javid et al., 2012; Townsley et al., 2005; Trimble et al., 1994). In the Javid study, researchers found that when trials were available, and patients were eligible for enrollment, phy- sicians discussed trial participation with 76 percent of patients under 65 years versus only 58 percent of patients over 65 years. However, several studies have found that older adults are as willing as younger adults to participate in clinical trials when given the opportunity by their clinicians (Kemeny et al., 2003; Kornblith et al., 2002). The following sections explore the inclusion of older adults and indi- viduals with multiple comorbidities in FDA registration trials and CER.

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216 DELIVERING HIGH-QUALITY CANCER CARE FDA Registration Trials Under FDA regulation, manufacturers are required to report clinical trial results by age4 and to include a “geriatric use” subsection in the label of their product that provides details on how to use the drug or biological product in older adults.5 The FDA has also issued numerous guidance documents that provide more comprehensive direction to manufacturers about the inclusion of older adults and individuals with comorbidities, but these are not binding legal documents. For example, FDA guidance encourages, but does not require, the routine and thorough evaluation of the effect of drugs in older adults, with the explicit purpose of providing clinicians with sufficient information on how to use drugs properly in this population (FDA, 1989, 2012c). The guidance states that patients in clinical studies should reflect the population that will receive the drug after it is marketed and notes that it is usually appropriate to include more than 100 geriatric patients in phase 2 and phase 3 trials (FDA, 2012c). It also emphasizes that there is no ra- tionale for excluding patients on the basis of advanced age alone, unless it will make it more difficult to interpret the study results. The guidance also encourages, but does not require, the inclusion of individuals over 75 years and suggests that exclusion criteria should focus on issues such as the presence of an illness that could make participation in a clinical trial dangerous or impact the individual’s ability to provide informed consent. To assist the FDA in determining how many older adults participated in a clinical trial, the guidance makes recommenda- tions on how to report the age of clinical trial participants (e.g., average age, age of the youngest and oldest participants, and the number of par- ticipants who fall into specific age categories) (FDA, 1988). In a report to the Government Accountability Office (GAO), the FDA noted that its medical officers routinely take the representation of older adults into consideration when reviewing drug applications (GAO, 2007). On the other hand, guidance documents have recognized that it can be challenging to include older adults with comorbidities and concomitant treatments in premarketing development studies and that data derived from these populations could be more appropriate for collection in the postmarketing context (FDA, 2012c). There is substantial evidence that older adults are routinely under- represented in registration trials for new cancer treatments. Talarico and 4  Investigational New Drug Applications and New Drug Applications, 63 Fed. Reg. 6854, 6862 (Feb 11, 1998) (codified at 21 CFR 314.50(d)(5), (vi)(a); 312.33(a)(2)(2007)). 5  Specific Requirements on Content and Format of Labeling for Human Prescription Drugs; Addition of “Geriatric Use” Subsection in the labeling, 62 Fed Reg. 45313, 4325 (August 27, 1997).

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THE EVIDENCE BASE FOR HIGH-QUALITY CANCER CARE 217 colleagues (2004) analyzed 28,766 cancer patients from 55 registration tri- als according to age distribution of 65 years and older, 70 years and older, and 75 years and older. They compared the participation rate of each age group to the corresponding rates in the U.S. cancer population. Individu- als age 65 years and older represented 36 percent of the trial participants compared with 60 percent of cancer patients, individuals 70 years and older represented 20 percent of trial participants and 46 percent of cancer patients, and individuals 75 years and older represented 9 percent of trial participants and 31 percent of cancer patients. In the GAO report mentioned above, the FDA reviewed 36 new drug applications (NDAs) from January 2001 through June 2003. They found that older adults (age 65 years and older) were included in at least one clinical drug trial supporting all 36 of the NDAs reviewed. The sponsors reported the number of older adults included in the clinical trials support- ing 28 of the NDAs. In these trials, older adults made up 33 percent of the populations studied (GAO, 2007). More recently, Scher and Hurria (2012) noted that in the geriatric us- age sections of the drug package inserts for 24 drugs approved for cancer treatment between 2007 and June 2010, only 33 percent of the participants were age 65 and older compared with 59 percent of the cancer popula- tion that is 65 years and older. Individuals with comorbidities are equally likely to be excluded from registration trials for new cancer treatments be- cause of the complexity of interpreting results when they are participants. Congress has regularly used market exclusivity to promote pub- lic health priorities in the pharmaceutical and biomedical sciences (Kesselheim, 2011). For example, the pediatric patent exclusivity provi- sions6 provide manufacturers with an additional 6 months of patent pro- tection for conducting clinical trials of their products in children. The law prevents generic versions of a drug from being marketed during those 6 months. Patent exclusivity applies regardless of the outcome of the trial and is not contingent on a labeling change for pediatric use. The goal of the law is to create an incentive for manufacturers to conduct research in children. This allows the government to subsidize research by providing patent extension, but without directly allocating any resources. The cost of the research is paid for by the manufacturers and passed on to the pa- tients and payers through higher drug prices for the additional 6 months (Kesselheim, 2011). A recent IOM committee concluded that studies conducted under the pediatric patent exclusivity laws “are yielding important information 6  Included in the FDA Modernization Act of 1997, Section 505A. Renewed in 2002 as part of the Best Pharmaceuticals for Children Act, and again in the Pediatric Research Equity Act of 2007.

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224 DELIVERING HIGH-QUALITY CANCER CARE labels are derived from clinicians’ interpretations of a patient’s experience in a clinical trial, as opposed to the patient’s own report of his or her expe- rience (Trotti et al., 2007). However, as mentioned above, research shows little agreement between the two types of reports, and clinicians often underestimate the severity of patients’ symptoms and miss preventable adverse events (Atkinson et al., 2011; Fromm et al., 2009). An example of a drug that recently had a label change based, in part, on PROs is Incyte Corporation’s Jakafi ruxolitinib in treating myelofibro- sis. Incyte Corporation measured patients’ night sweats, itching, abdomi- nal discomfort, pain under the ribs, early satiety, and bone or muscle pain when using the drug and found that the drug relieved these symptoms (McCallister and Usdin, 2011). However, this type of PRO evaluation and labeling outcome is the exception rather than the rule, and there is a great need for expanding the measurement of PROs in the context of drug de- velopment (Basch, 2013). The NCI supports the use of PROs for identifying adverse events in clinical trials and considers understanding patients’ reported experiences with their disease an important goal of research (Clauser et al., 2007). Most adverse events in cancer clinical trials are currently obtained, inter- preted, and reported by clinicians using the NCI’s Common Terminology Criteria for Adverse Events (CTCAE). However, in October 2008, the NCI issued a contract to develop a PRO version of CTCAE, known as the PRO-CTCAE. This project is not yet complete, but information regarding its development is available on the NCI website. The latest version of the PRO-CTCAE includes 81 symptoms appropriate for patient reporting, and its multiple language translations are being validated (NCI, 2012a). Similarly, NIH has developed the Patient Reported Outcomes Measure- ment Information System (PROMIS), which is a set of measures that cap- ture patients’ physical, mental, and social well-being but is not specific to cancer (NIH, 2013). The measures included in these tools could fulfill part of the committee’s recommendation to develop common data ele- ments that should be collected in all phase 3 trials. The NCI should use PROs to gather information from patients, including quality-of-life data, functional status, and adverse events. Biomarkers, Patient Characteristics, and Behavioral Data A recent IOM report recognized the growing need for correlative and translational studies that measure the relationship between biomarkers or other patient characteristics collected during clinical trials and health outcomes (IOM, 2010b). This research is important because it is increas- ingly recognized that patient characteristics and behaviors have an im- pact on cancer outcomes and will play an important role in personalized

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THE EVIDENCE BASE FOR HIGH-QUALITY CANCER CARE 225 cancer treatment (Antoni et al., 2006; Goodwin et al., 2010). Examples of characteristics that impact patient outcomes in cancer include demo- graphics (e.g., age, sex, race/ethnicity, marital status, education); indi- vidual genetics (see discussion in Chapter 2); functional status; comorbid conditions; behavioral risk factors (e.g., tobacco use, alcohol use, human immunodeficiency virus and human papillomavirus status, sedentary lifestyle, insomnia); medications and supplements; psychological health status; and physiological health status (e.g., inflammation, coagulation) (Ganz, 2012). For example, tobacco exposure can influence drug metabolism, re- sponse to and toxicity of treatment, and the biological aggressiveness of cancer. Correlative research has led to the observation that individuals with non-small-cell lung cancer who never smoked have a significantly greater likelihood of benefiting from an epidermal growth factor receptor tyrosine kinase inhibitor than do individuals who have smoked (Faehling et al., 2010). However, despite the impact of this observation on clinical practice, most Cooperative Group trials do not collect data on partici- pants’ tobacco exposure as part of their clinical trials (Peters et al., 2012). Clinical researchers are also inconsistent in collecting other biomarker, patient characteristics, and behavioral data. The importance of this type of data is particularly salient in older adults with cancer because of the need to identify risk factors for treatment toxicity and to develop more complete geriatric assessment variables (see discussion on geriatric as- sessments in Chapter 2) (Extermann and Hurria, 2007; Extermann et al., 2012; Hurria et al., 2011). Cost Data As noted in Chapter 2, the cost of cancer care is spiraling out of control, yet there has been little effort to regularly collect cost data dur- ing clinical trials. Without this type of data, it is challenging to conduct cost-effectiveness analyses. Thus, policy makers cannot make informed decisions about addressing the unsustainable cost of care, and it is dif- ficult for patients to take the cost of care into account in their medical decision-making process (see Chapter 3). Improving the Use of Information Technology It is impractical to use a clinical trial to answer all research questions relevant to improving the quality of cancer care. The average cost of a large randomized clinical trial addressing a CER question ranges from $15 to $20 million (Holve and Pittman, 2011). In addition, clinical trials do not address all clinically relevant populations, limiting their generaliz-

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226 DELIVERING HIGH-QUALITY CANCER CARE ability. Clinical trials only cover a limited period of time and thus may not identify long-term side effects. They also often fail to make comparisons relevant to answering questions that are important to patients and clini- cians (IOM, 2012a). Multiple IOM reports have emphasized the need to match research questions with the most appropriate research method (IOM, 2008b, 2011a, 2012a). For example, clinical trials are valuable for answering questions about the efficacy of screening, preventive, and therapeutic interventions, while observational studies can answer questions about potential harms, long-term outcomes, and the use of interventions in real-world scenarios. In Chapter 6, the committee recommends the development of a learn- ing health care system for cancer, which is an IT system that continually and automatically collects and compiles information from clinical prac- tice, disease registries, clinical trials, and other sources in order to deliver the best, most up-to-date care, personalized for each patient. One of the outcomes of this system would be an enormous clinical data resource that could be used for observational research. The potential for a learning can- cer care system to improve research and the generation of new knowledge about cancer care is enormous. A fully operational learning health care system would allow re- searchers to use data from electronic health records (EHRs), the SEER- Medicare database, Cooperative Group trials, FDA registration trials, cancer registries, and other sources to conduct systematic reviews and meta-analyses, pooled analyses of patient-level data from many clinical trials, and other types of observational and nonexperimental studies. It would also allow researchers to link patient-level data from multiple sources longitudinally and facilitate the surveillance of long-term side effects and health outcomes from various cancer care plans, as well as capture place of death. In addition, implementation of a learning health care system would overcome many clinical trial limitations. It would provide researchers with access to data from a large, diverse, population (by gender, geog- raphy, ethnicity, age, education, and socioeconomic status), which could lead to the identification of subgroup variations. This would be particu- larly helpful in studying older adults with cancer because the learning health care system would include data on individuals with multiple co- morbidities, concomitant medications, and those who are in the oldest age ranges. A learning health care system would also benefit cancer research more broadly by providing data on off-label prescribing, which accounts for the majority of cancer treatments, as well as on new technologies and surgical techniques not subject to strict regulatory review (Abernethy et al., 2010;

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THE EVIDENCE BASE FOR HIGH-QUALITY CANCER CARE 227 Etheredge, 2010; IOM, 2010a, 2012a,b). It would also provide information on quality of life and functional status, which would be important to pa- tients’ decision making (see discussion in Chapter 3) if this information was regularly collected in clinical trials (see recommendation above on improving the depth of information collected in clinical research) and in EHRs. The major limitation of this type of research is that data from many of these sources are not collected as systematically as data from clinical trials. As a result, there is the potential for bias and drawing erroneous conclusions. Researchers will need to develop analytic methods to adjust for these data limitations. In addition, this research cannot analyze inter- ventions not already used in clinical practice and thus cannot serve as a substitute for premarket approval of new drugs, biologics, or devices (Armstrong, 2012). Implementation challenges, technical challenges, and ethical oversight challenges to achieving a learning health care system for cancer are discussed in Chapter 6. Summary and Recommendations Because a high-quality cancer care delivery system uses results from scientific research, such as clinical trials and CER, to inform medical deci- sions, the committee’s conceptual framework (see Figure S-2) depicts the evidence base as supporting patient-clinician interactions. The committee envisions clinical research that gathers evidence of the benefits and harms of various treatment options, so that patients, in consultation with their clinicians, can make treatment decisions that are consistent with their needs, values, and preferences. Currently, many studies are not supported by sufficient evidence. Additionally, research participants are often not representative of the population with the disease, which makes it difficult to generalize the research results to a specific patient. Another limitation of the current evidence base is that it frequently does not capture information about the impact of a treatment regimen on quality of life, functional and cognitive status, symptoms, and overall patient experience with the disease. Given that the majority of cancer patients are over 65 years and have comorbid conditions complicated by other health (e.g., physical and cognitive defi- cits) and social (e.g., limited or absent social support, low health literacy) risks, the committee is particularly concerned about the lack of clinical research focused on older adults and individuals with multiple chronic diseases.

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228 DELIVERING HIGH-QUALITY CANCER CARE Recommendation 5: Evidence-Based Cancer Care Goal: Expand the breadth of data collected on cancer interventions for older adults and individuals with multiple comorbid conditions. To accomplish this: •  he National Cancer Institute, the Agency for Healthcare Re- T search and Quality, the Patient-Centered Outcomes Research Institute, and other comparative effectiveness research funders should require researchers evaluating the role of standard and novel interventions and technologies used in cancer care to include a plan to study a population that mirrors the age distri- bution and health risk profile of patients with the disease. •  ongress should amend patent law to provide patent extensions C of up to 6 months for companies that conduct clinical trials of new cancer treatments in older adults or patients with multiple comorbidities. Recommendation 6: Evidence-Based Cancer Care Goal: Expand the depth of data available for assessing interventions. To accomplish this: •  he National Cancer Institute should build on ongoing efforts T and work with other federal agencies, the Patient-Centered Outcomes Research Institute, clinical and health services re- searchers, clinicians, and patients to develop a common set of data elements that captures patient-reported outcomes, relevant patient characteristics, and health behaviors that researchers should collect from randomized clinical trials and observa- tional studies. References Abernethy, A. P., L. M. Etheredge, P. A. Ganz, P. Wallace, R. R. German, C. Neti, P. B. Bach, and S. B. Murphy. 2010. Rapid-learning system for cancer care. Journal of Clinical Oncol- ogy 28(27):4268-4274. Abrams, J., R. Erwin, G. Fyfe, and R. L. Schilsky. 2010. Data submission standards and evi- dence requirements. Oncologist 15(5):488-491. AHRQ (Agency for Healthcare Research and Quality). 2011. Special emphasis notice: AHRQ announces interest in priority populations research. http://grants.nih.gov/grants/guide/ notice-files/NOT-HS-11-014.html (accessed March 22, 2013).

OCR for page 207
THE EVIDENCE BASE FOR HIGH-QUALITY CANCER CARE 229 ———. 2013a. Who is involved in the Effective Health Care Program. http://www.effective healthcare.ahrq.gov/index.cfm/who-is-involved-in-the-effective-health-care- program1 (accessed July 1, 2013). ———. 2013b. Search for research summaries, reviews, and reports. http://www.effective healthcare.ahrq.gov/index.cfm/search-for-guides-reviews-and-reports (accessed July 1, 2013). Alecxih, L., S. Shen, I. Chan, D. Taylor, and J. Drabek. 2010. Individuals living in the commu- nity with chronic conditions and functional limitations: A closer look. http://aspe.hhs.gov/ daltcp/reports/2010/closerlook.pdf (accessed March 22, 2013). Antoni, M. H., S. K. Lutgendorf, S. W. Cole, F. S. Dhabhar, S. E. Sephton, P. G. McDonald, M. Stefanek, and A. K. Sood. 2006. The influence of bio-behavioural factors on tumour biology: Pathways and mechanisms. Nature Reviews. Cancer 6(3):240-248. Armstrong, K. 2012. Methods in comparative effectiveness research. Journal of Clinical Oncol- ogy 30(34):4208-4214. Atkinson, T. M., Y. Li, C. W. Coffey, L. Sit, M. Shaw, D. Lavene, A. V. Bennett, M. Fruscione, L. Rogak, and J. Hay. 2011. Reliability of adverse symptom event reporting by clinicians. Quality of Life Research:1-6. Baker-Smith, C. M., D. K. Benjamin, Jr., H. G. Grabowski, E. D. Reid, B. Mangum, J. V. Goldsmith, M. D. Murphy, R. Edwards, E. L. Eisenstein, J. Sun, R. M. Califf, and J. S. Li. 2008. The economic returns of pediatric clinical trials of antihypertensive drugs. American Heart Journal 156(4):682-688. Basch, E. 2013. Toward patient-centered drug development in oncology. New England Journal of Medicine 369(5):397-400. Basch, E., A. Iasonos, T. McDonough, A. Barz, A. Culkin, M. G. Kris, H. I. Scher, and D. Schrag. 2006. Patient versus clinician symptom reporting using the National Cancer Institute Common Terminology Criteria for Adverse Events: Results of a questionnaire- based study. Lancet Oncology 7(11):903-909. Basch, E., P. Torda, and K. Adams. 2013. Standards for patient-reported outcome-based per- formance measures. Journal of the American Medical Association 310(2):139-140. Belew, Y. 2008. Clinical review for Viramune (nevirapine). NDA 20636/20933. June 21. Sil- ver Spring, MD: Food and Drug Administration. http://www.fda.gov/downloads/ Drugs/ DevelopmentApprovalProcess/DevelopmentResources/ucm072777.pdf (ac- cessed March 23, 2012). Benjamin, D. K., Jr., P. B. Smith, P. Jadhav, J. V. Gobburu, M. D. Murphy, V. Hasselblad, C. Baker-Smith, R. M. Califf, and J. S. Li. 2008. Pediatric antihypertensive trial failures: Analysis of end points and dose range. Hypertension 51(4):834-840. Benjamin, D. K., Jr., P. B. Smith, M. J. Sun, M. D. Murphy, D. Avant, L. Mathis, W. Rodriguez, R. M. Califf, and J. S. Li. 2009. Safety and transparency of pediatric drug trials. Archives of Pediatric & Adolescent Medicine 163(12):1080-1086. Boots, I., R. N. Sukhai, R. H. Klein, R. A. Holl, J. M. Wit, A. F. Cohen, and J. Burggraaf. 2007. Stimulation programs for pediatric drug research: Do children really benefit? European Journal of Pediatrics 166(8):849-855. Cerreta, F., H. G. Eichler, and G. Rasi. 2012. Drug policy for an aging population: The Euro- pean Medicines Agency’s geriatric medicines strategy. New England Journal of Medicine 367(21):1972-1974. Clancy, C., and F. S. Collins. 2010. Patient-Centered Outcomes Research Institute: The inter- section of science and health care. Science Translational Medicine 2(37):37cm18. Clauser, S. B., P. A. Ganz, J. Lipscomb, and B. B. Reeve. 2007. Patient-reported outcomes assessment in cancer trials: Evaluating and enhancing the payoff to decision making. Journal of Clinical Oncology 25(32):5049-5050.

OCR for page 207
230 DELIVERING HIGH-QUALITY CANCER CARE Cox, K., and J. McGarry. 2003. Why patients don’t take part in cancer clinical trials: An over- view of the literature. European Journal of Cancer Care (Engl) 12(2):114-122. Crewalk, J.-A. 2008. Clinical review for PegIntron (peginterferon alfa-2b). BLA 103949. De- cember 8. Silver Spring, MD: Food and Drug Administration. http://www.fda. gov/downloads/Drugs/DevelopmentApprovalProcess/DevelopmentResources/ UCM171341.pdf (accessed March 23, 2012). Dhruva, S. S., and R. F. Redberg. 2008. Variations between clinical trial participants and Medicare beneficiaries in evidence used for Medicare national coverage decisions. Archives of Internal Medicine 168(2):136-140. EDICT (Eliminating Disparities in Clinical Trials). 2008. The EDICT project: Policy recommen- dations to eliminate disparities in clinical trials. Houston, TX: EDICT Project. El Dib, R. P., A. N. Atallah, and R. B. Andriolo. 2007. Mapping the Cochrane evidence for decision making in health care. Journal of Evaluation in Clinical Practice 13:689-692. Etheredge, L. M. 2010. Creating a high-performance system for comparative effectiveness research. Health Affairs (Millwood) 29(10):1761-1767. Extermann, M., and A. Hurria. 2007. Comprehensive geriatric assessment for older patients with cancer. Journal of Clinical Oncology 25(14):1824-1831. Extermann, M., I. Boler, R. R. Reich, G. H. Lyman, R. H. Brown, J. DeFelice, R. M. Levine, E. T. Lubiner, P. Reyes, F. J. Schreiber, 3rd, and L. Balducci. 2012. Predicting the risk of chemotherapy toxicity in older patients: The Chemotherapy Risk Assessment Scale for High-age Patients (crash) score. Cancer 118(13):3377-3386. Faehling, M., R. Eckert, S. Kuom, T. Kamp, K. M. Stoiber, and C. Schumann. 2010. Benefit of erlotinib in patients with non-small-cell lung cancer is related to smoking status, gender, skin rash and radiological response but not to histology and treatment line. Oncology 78(3-4):249-258. FDA (Food and Drug Administration). 1988. Guideline for the format and content of the clinical and statistical sections of an application. http://www.fda.gov/downloads/Drugs/Guidance ComplianceRegulatoryInformation/Guidances/UCM071665.pdf (accessed November 30, 2012). ———. 1989. Guidance for industry. Guidelines for the study of drugs likely to be used in the elderly. http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatory Information/Guidances/ucm072048.pdf (accessed November 30, 2012). ———. 1998. FDAMA: Women and minorities guidance requirements. http://www.fda. gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ ucm080616.pdf (accessed July 1, 2013). ———. 2001. The pediatric exclusivity provision: January 2001 status report to Congress. http:// www.fda.gov/downloads/drugs/developmentapprovalprocess/development resources/ucm049915.pdf (accessed December 3, 2012). ———. 2006. Drug administration: Guidance for industry: Patient-reported outcome measures—use in medical product development to support labeling claims. Food and Drug Administration. Health Quality of Life Outcomes 4:79. ———. 2009. Guidance for industry. Patient-reported outcome measures: Use in medical prod- uct development to support labeling claims. http://www.fda.gov/downloads/Drugs/ GuidanceComplianceRegulatoryInformation/Guidances/UCM193282.pdf (accessed December 3, 2012). ———. 2012a. Device approvals and clearances. http://www.fda.gov/medicaldevices/ productsandmedicalprocedures/deviceapprovalsandclearances/default.htm (accessed November 29, 2012). ———. 2012b. The FDA’s drug review process: Ensuring drugs are safe and effective. http://www. fda.gov/Drugs/ResourcesForYou/Consumers/ucm143534.htm (accessed November 29, 2012).

OCR for page 207
THE EVIDENCE BASE FOR HIGH-QUALITY CANCER CARE 231 ———. 2012c. Guidance for industry. E7 studies in support of special populations: Geriatrics. Questions and answers. http://www.fda.gov/downloads/Drugs/GuidanceCompliance RegulatoryInformation/Guidances/UCM189544.pdf (accessed November 30, 2012). Feeney, J. 2000. Medical review for Neurontin (gabapentin). NDA 21216. October 6. Silver Spring, MD: Food and Drug Administration. http://www.accessdata.fda.gov/drugsat fda_docs/nda/2000/21- 16.pdf_Neurontin_Medr_P1.pdf (accessed March 23, 2012). Fromme, E. K., K. M. Eilers, M. Mori, Y. C. Hsieh, and T. M. Beer. 2004. How accurate is clinician reporting of chemotherapy adverse effects? A comparison with patient- reported symptoms from the quality-of-life questionnaire c30. Journal of Clinical Oncol- ogy 22(17):3485-3490. Gabry, K. E., and H. V. Joffe. 2008. Clinical review of Apidra (insulin glulisine). NDA 21629. October 2. Silver Spring, MD: Food and Drug Administration. http://www.fda.gov/ downloads/Drugs/DevelopmentApprovalProcess/DevelopmentResources/ ucm072460.pdf (accessed March 23, 2012). Ganz, P. A. 2012. Host factors, behaviors, and clinical trials: Opportunities and challenges. Journal of Clinical Oncology 30(23):2817-2819. GAO (Government Accountability Office). 2007. Elderly persons in clinical drug trials. http:// www.gao.gov/assets/100/95182.pdf (accessed December 10, 2012). Genentech. 2010. Prescribing information for Xolair (omalizumab). South San Francisco, CA: Genentech. http://www.gene.com/gene/products/information/pdf/xolair-prescribing. pdf (accessed March 23, 2012). Goodwin, P. J., J. A. Meyerhardt, and S. D. Hursting. 2010. Host factors and cancer outcome. Journal of Clinical Oncology 28(26):4019-4021. Grieve, J., J. Tordoff, D. Reith, and P. Norris. 2005. Effect of the pediatric exclusivity provision on children’s access to medicines. British Journal of Clinical Pharmacology 59(6):730-735. Hahn, O. M., and R. L. Schilsky. 2012. Randomized controlled trials and comparative ef- fectiveness research. Journal of Clinical Oncology 30(34):4194-4201. Herrera, A. P., S. A. Snipes, D. W. King, I. Torres-Vigil, D. S. Goldberg, and A. D. Weinberg. 2010. Disparate inclusion of older adults in clinical trials: Priorities and opportunities for policy and practice change. American Journal of Public Health 100 (Suppl 1):S105-S112. Holve, E., and P. Pittman. 2011. The cost and volume of comparative effectiveness research. In Learning what works: Infrastructure required for comparative effectiveness research (work- shop summary), edited by L. Olsen, C. Grossman, and J. M. McGinnis. Washington, DC: The National Academies Press. Pp. 89-96. Hurria, A., K. Togawa, S. G. Mohile, C. Owusu, H. D. Klepin, C. P. Gross, S. M. Lichtman, A. Gajra, S. Bhatia, V. Katheria, S. Klapper, K. Hansen, R. Ramani, M. Lachs, F. L. Wong, and W. P. Tew. 2011. Predicting chemotherapy toxicity in older adults with cancer: A prospective multicenter study. Journal of Clinical Oncology 29(25):3457-3465. Hutchins, L. F., J. M. Unger, J. J. Crowley, C. A. Coltman, Jr., and K. S. Albain. 1999. Un- derrepresentation of patients 65 years of age or older in cancer-treatment trials. New England Journal of Medicine 341(27):2061-2067. IOM (Institute of Medicine). 2008a. Improving the quality of cancer clinical trials: Workshop summary. Washington, DC: The National Academies Press. ———. 2008b. Knowing what works in health care: A roadmap for the nation. Washington, DC: The National Academies Press. ———. 2009a. Initial national priorities for comparative effectiveness research. Washington, DC: The National Academies Press. ———. 2009b. Multi-center phase III clinical trials and NCI cooperative groups: Workshop sum- mary. Washington, DC: The National Academies Press. ———. 2010a. A foundation for evidence-driven practice: A rapid learning system for cancer care: Workshop summary. Washington, DC: The National Academies Press.

OCR for page 207
232 DELIVERING HIGH-QUALITY CANCER CARE ———. 2010b. A national cancer clinical trials system for the 21st century: Reinvigorating the NCI Cooperative Group Program. Washington, DC: The National Academies Press. ———. 2011a. Finding what works in health care: Standards for systematic reviews. Washington, DC: The National Academies Press. ———. 2011b. Medical devices and the public’s health: The FDA 510(k) clearance process at 35 years. Washington, DC: The National Academies Press. ———. 2012a. Best care at lower cost: The path to continously learning health care in America. Washington, DC: The National Academies Press. ———. 2012b. Informatics needs and challenges in cancer research: Workshop summary. Washing- ton, DC: The National Academies Press. ———. 2012c. Safe and effective medicines for children: Pediatric studies conducted under the Best Pharmaceuticals for Children Act and the Pediatric Research Equity Act. Washington, DC: The National Academies Press. ———. 2013. Delivering affordable cancer care in the 21st century: Workshop summary. Washing- ton, DC: The National Academies Press. IOM and NRC (National Research Council). 1999. Ensuring quality cancer care. Washington, DC: National Academy Press. Javid, S. H., J. M. Unger, J. R. Gralow, C. M. Moinpour, A. J. Wozniak, J. W. Goodwin, P. N. Lara, Jr., P. A. Williams, L. F. Hutchins, C. C. Gotay, and K. S. Albain. 2012. A prospec- tive analysis of the influence of older age on physician and patient decision-making when considering enrollment in breast cancer clinical trials (SWOG S0316). Oncologist 17(9):1180-1190. Katz, R. G. 2000. Approval letter for Neurontin (gabapentin). NDA 21216/20235/20882/21129. October 12. Silver Spring, MD: Food and Drug Administration. http://www.accessdata. fda.gov/drugsatfda_docs/nda/2000/21-216.pdf_Neurontin_Approv.pdf (accessed April 3, 2012). Kemeny, M. M., B. L. Peterson, A. B. Kornblith, H. B. Muss, J. Wheeler, E. Levine, N. Bartlett, G. Fleming, and H. J. Cohen. 2003. Barriers to clinical trial participation by older women with breast cancer. Journal of Clinical Oncology 21(12):2268-2275. Kesselheim, A. S. 2011. An empirical review of major legislation affecting drug devel- opment: Past experiences, effects, and unintended consequences. Milbank Quarterly 89(3):450-502. Kornblith, A. B., M. Kemeny, B. L. Peterson, J. Wheeler, J. Crawford, N. Bartlett, G. Fleming, S. Graziano, H. Muss, and H. J. Cohen. 2002. Survey of oncologists’ perceptions of barriers to accrual of older patients with breast carcinoma to clinical trials. Cancer 95(5):989-996. Kumar, A., H. P. Soares, L. Balducci, and B. Djulbegovic. 2007. Treatment tolerance and efficacy in geriatric oncology: A systematic review of phase III randomized trials conducted by five National Cancer Institute-sponsored cooperative groups. Journal of Clinical Oncology 25(10):1272-1276. Last, J. M., ed. 1995. A dictionary of epidemiology. 3rd ed. New York: Oxford University Press. Lewis, J. H., M. L. Kilgore, D. P. Goldman, E. L. Trimble, R. Kaplan, M. J. Montello, M. G. Housman, and J. J. Escarce. 2003. Participation of patients 65 years of age or older in cancer clinical trials. Journal of Clinical Oncology 21(7):1383-1389. Li, J. S., E. L. Eisenstein, H. G. Grabowski, E. D. Reid, B. Mangum, K. A. Schulman, J. V. Goldsmith, M. D. Murphy, R. M. Califf, and D. K. Benjamin, Jr. 2007. Economic return of clinical trials performed under the pediatric exclusivity program. Journal of the American Medical Association 297(5):480-488. Lyman, G. H., and M. Levine. 2012. Comparative effectiveness research in oncology: An overview. Journal of Clinical Oncology 30(34):4181-4184.

OCR for page 207
THE EVIDENCE BASE FOR HIGH-QUALITY CANCER CARE 233 McCallister, E., and S. Usdin. 2011. A PROfessional trial. BioCentury on Business 19(49): A1-A4. Meropol, N. J. 2012. Comparative effectiveness research to inform medical decisions: The need for common language. Journal of Clinical Oncology 30:1-2. Meropol, N. J., D. Schrag, T. J. Smith, T. M. Mulvey, R. M. Langdon, Jr., D. Blum, P. A. Ubel, and L. E. Schnipper. 2009. American Society of Clinical Oncology guidance statement: The cost of cancer care. Journal of Clinical Oncology 27(23):3868-3874. Meyer, R. 2004. Approval letter for Apidra (insulin glulisine (rDNA origin)). NDA 21629. April 16. Silver Spring, MD: Food and Drug Administration. http://www.accessdata.fda. gov/drugsatfda_docs/appletter/2004/21629ltr.pdf (accessed April 3, 2012). Milne, C. P. 2002. Exploring the frontiers of law and science: FDAMA’s pediatric studies incentive. Food Drug Law Journal 57(3):491-517. Miriovsky, B. J., L. N. Shulman, and A. P. Abernethy. 2012. Importance of health informa- tion technology, electronic health records, and continuously aggregating data to com- parative effectiveness research and learning health care. Journal of Clinical Oncology 30(34):4243-4248. NCI (National Cancer Institute). 2012a. Patient-reported outcomes version of the common ter- minology criteria for adverse events (pro-ctcae). http://outcomes.cancer.gov/tools/pro- ctcae_fact_sheet.pdf (accessed March, 2012). ———. 2012b. Prioritization/scientific quality initiatives. Place Published. http://transforming trials.cancer.gov/initiatives/ctwg/prioritization (accessed December 6, 2012). ———. 2012c. Transforming the NCI clinical trial enterprise. http://transformingtrials.cancer. gov/initiatives/overview (accessed December 6, 2012). Nelson, R. E., C. McAdam-Marx, M. L. Evans, R. Ward, B. Campbell, D. Brixner, and J. Lafleur. 2011. Patent extension policy for paediatric indications: An evaluation of the impact within three drug classes in a state Medicaid program. Applied Health Economics and Health Policy 9(3):171-181. NIH (National Institutes of Health). 2001. NIH policy and guidelines on the inclusion of women and minorities as subjects in clinical research. http://grants.nih.gov/grants/guide/notice- files/NOT-OD-02-001.html (accessed November 30, 2012). ———. 2012. NIH Toolbox for the Assessment of Neurological and Behavioral Function. http:// www.nihtoolbox.org/Pages/default.aspx (accessed July 3, 2013). ———. 2013. PROMIS® overview. http://www.nihpromis.org/about/overview (accessed July 3, 2013). Pasquali, S. K., D. S. Burstein, D. K. Benjamin, Jr., P. B. Smith, and J. S. Li. 2010. Globaliza- tion of pediatric research: Analysis of clinical trials completed for pediatric exclusivity. Pediatrics 126(3):e687-e692. PCORI (Patient-Centered Outcomes Research Institute). 2012a. About us. http://www.pcori. org/about-us/landing (accessed November 29, 2012). ———. 2012b. PCORI methodology standards. http://www.pcori.org/assets/PCORI- Methodology-Standards1.pdf (accessed July 1, 2013). Peters, E. N., E. Torres, B. A. Toll, K. M. Cummings, E. R. Gritz, A. Hyland, R. S. Herbst, J. R. Marshall, and G. W. Warren. 2012. Tobacco assessment in actively accruing National Cancer Institute Cooperative Group Program clinical trials. Journal of Clinical Oncology 30(23):2869-2875. Ramsey, S. D., S. D. Sullivan, S. D. Reed, Y. C. Tina Shih, K. Schaecher, R. Dhanda, D. Patt, K. Pendergrass, M. Walker, J. Malin, L. Schwartzberg, K. Neumann, E. Yu, A. Ravelo, and A. Small. 2013. Oncology comparative effectiveness research: A multistakeholder perspective on principles for conduct and reporting. Oncologist 18(6):760-767.

OCR for page 207
234 DELIVERING HIGH-QUALITY CANCER CARE Risso, S. T. 2003. Approval letter for Xolair (omalizumab). BLA 103976/0. June 20. Silver Spring, MD: Food and Drug Administration. http://www.accessdata.fda.gov/drugsatfda_ ocs/appletter/2003/omalgen062003L.htm (accessed April 3, 2012). Rodriguez, W., A. Selen, D. Avant, C. Chaurasia, T. Crescenzi, G. Gieser, J. Di Giacinto, S. M. Huang, P. Lee, L. Mathis, D. Murphy, S. Murphy, R. Roberts, H. C. Sachs, S. Suarez, V. Tandon, and R. S. Uppoor. 2008. Improving pediatric dosing through pediatric initia- tives: What we have learned. Pediatrics 121(3):530-539. Scher, K. S., and A. Hurria. 2012. Under-representation of older adults in cancer registration trials: Known problem, little progress. Journal of Clinical Oncology 30(17):2036-2038. Schilsky, R. L. 2013. Publicly funded clinical trials and the future of cancer care. Oncologist 18(2):232-238. Somkin, C. P., A. Altschuler, L. Ackerson, A. M. Geiger, S. M. Greene, J. Mouchawar, J. Holup, L. Fehrenbacher, A. Nelson, A. Glass, J. Polikoff, S. Tishler, C. Schmidt, T. Field, and E. Wagner. 2005. Organizational barriers to physician participation in cancer clinical trials. American Journal of Managed Care 11(7):413-421. Starke, P. 2009. Clinical review for Xolair (omalizumab). BLA 103976/5149. December 4. Silver Spring, MD: Food and Drug Administration. http://www.fda.gov/downloads/Drugs/ DevelopmentApprovalProcess/DevelopmentResources/UCM202179.pdf (accessed April 3, 2012). Talarico, L., G. Chen, and R. Pazdur. 2004. Enrollment of elderly patients in clinical trials for cancer drug registration: A 7-year experience by the U.S. Food and Drug Administra- tion. Journal of Clinical Oncology 22(22):4626-4631. Tinetti, M. E., and S. A. Studenski. 2011. Comparative effectiveness research and patients with multiple chronic conditions. New England Journal of Medicine 364(26):2478-2481. Townsley, C. A., R. Selby, and L. L. Siu. 2005. Systematic review of barriers to the recruit- ment of older patients with cancer onto clinical trials. Journal of Clinical Oncology 23(13):3112-3124. Trimble, E. L., C. L. Carter, D. Cain, B. Freidlin, R. S. Ungerleider, and M. A. Friedman. 1994. Representation of older patients in cancer treatment trials. Cancer 74(7 Suppl):2208-2214. Trotti, A., A. D. Colevas, A. Setser, and E. Basch. 2007. Patient-reported outcomes and the evolution of adverse event reporting in oncology. Journal of Clinical Oncology 25(32): 5121-5127. Van Spall, H. G., A. Toren, A. Kiss, and R. A. Fowler. 2007. Eligibility criteria of randomized controlled trials published in high-impact general medical journals: A systematic sam- pling review. Journal of the American Medical Association 297(11):1233-1240. Villas Boas, P. J., R. S. Spagnuolo, A. Kamegasawa, L. G. Braz, A. Polachini do Valle, E. C. Jorge, H. H. Yoo, A. J. Cataneo, I. Correa, F. B. Fukushima, P. do Nascimento, N. S. Modolo, M. S. Teixeira, E. I. de Oliveira Vidal, S. R. Daher, and R. El Dib. 2012. System- atic reviews showed insufficient evidence for clinical practice in 2004: What about in 2011? The next appeal for the evidence-based medicine age. Journal of Evaluating Clinical Practice 19(4):633-637.