“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
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 management 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 sufficiently 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 population 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 disease 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 capacity, 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 adequately 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 combination 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 approaches. 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 making in cancer care is generated and discusses the need to improve the
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 inform 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 committee identifies two recommendations to improve the evidence base for high-quality cancer care.
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 improve patient outcomes. The Food and Drug Administration (FDA), the federal agency charged with regulating pharmaceuticals and medical devices, requires manufacturers to submit scientific evidence that establishes the safety and effectiveness of their products prior to making them available 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 processes 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 ensure safety and effectiveness (IOM, 2011b). The IOM recommended that the FDA design a new medical-device regulatory framework. Neverthe-
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 different 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 monitored 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. Manufacturers 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 interest 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:
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 technologies for diagnosis, staging, and monitoring of all cancers; use of biomarker 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) reinforced 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 studies 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).
Experimental study: A study in which the investigators actively intervene to test a hypothesis.
• Controlled trials are experimental studies in which a group receives the 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.
• In a randomized controlled trial (RCT), participants are randomly allocated to the experimental group or the comparison group. Cluster randomized 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.
• In prospective observational studies, the exposure of interest is studied using data stored in registries, which can require years to accumulate the needed numbers of patients and outcomes.
• In cohort studies, groups with certain characteristics or receiving certain 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).
• In case-control studies, groups with and without an event or outcome are examined to see whether a past exposure or characteristic is more prevalent in one group than in the other.
• In cross-sectional studies, the prevalence of an exposure of interest is 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 question 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 include a quantitative synthesis (meta-analysis) of the results from separate studies.
• A meta-analysis is an SR that uses statistical methods to combine quantitatively 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.
population and subpopulation levels, and measure benefits and risks that are important to patients. The Cooperative Groups’ inclusion of the Community 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 improvements 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 Cooperative 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 literature 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, devices, and biological products; and (4) the John M. Eisenberg Clinical Decisions 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).
For clinical research to improve the quality of cancer care, researchers 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
participation in clinical trials and requires insurers to cover research participants’ 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 majority 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 willing 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 extrapolate from clinical trials conducted on younger, healthier adults and apply that information to older adults, hoping that the information will be relevant 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.”
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 eligibility 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 research. Trials often have stringent eligibility criteria with regard to comorbidities, 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). Approximately 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, physicians 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 individuals with multiple comorbidities in FDA registration trials and CER.
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 rationale 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 recommendations 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 participants 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 underrepresented 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).
colleagues (2004) analyzed 28,766 cancer patients from 55 registration trials 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. Individuals 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 supporting 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 usage 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 population that is 65 years and older. Individuals with comorbidities are equally likely to be excluded from registration trials for new cancer treatments because of the complexity of interpreting results when they are participants.
Congress has regularly used market exclusivity to promote public health priorities in the pharmaceutical and biomedical sciences (Kesselheim, 2011). For example, the pediatric patent exclusivity provisions6 provide manufacturers with an additional 6 months of patent protection 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 patients 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.
Pediatric Studies Support Safety and Efficacy
Insulin glulisine (Apidra), a recombinant, rapid-acting human insulin analog, was approved in 2004 for treatment of type 1 diabetes mellitus in adults, with a requirement for a study with children ages 5 to 17 years (Meyer, 2004). In 2008, on the basis of the findings of one previously submitted pharmacokinetic/ pharmacodynamic study and one new safety and efficacy study, the Food and Drug Administration (FDA) approved use of the product by children ages 4 to 17 years, the period of peak onset for this disease (Gabry and Joffe, 2008).
Safe and Effective Dosing in Children Differs from Expectations for Youngest Children
Gabapentin (Neurontin) was first approved in 1993. The FDA requested studies under BPCA in 1999, and the drug was approved in 2000 as adjunctive treatment of partial seizures in children ages 3 years and older (Katz, 2000). Based on staff analyses of pharmacokinetic data, the FDA concluded that children under 5 years of age required higher than anticipated doses (Feeney, 2000). Findings from the study for the 3- to 12-year-old age group also led to a warning on the product’s label about adverse neuropsychiatric events, such as concentration problems, hostility, and hyperactivity.
Drug Affects Growth and Development
Pegylated interferon alfa 2b (PegIntron) in combination with ribavirin (Rebetol) was approved in June 2008 for the treatment of chronic hepatitis C virus infection in patients ages 18 years or older, with deferral of PREA-required studies for children ages 3 years or older. In December 2008, after the required studies
to guide clinical care for children” (IOM, 2012c, p. 26). This committee summarized knowledge contributed by studies conducted under federal programs designed to increase research in children, including the pediatric patent exclusivity (see Box 5-2).
In addition, the pediatric patent exclusivity has contributed to researchers conducting more than 300 pediatric studies between 1997 and 2002 (Li et al., 2007; Milne, 2002). These studies have led to revised labeling of dosing, safety, efficacy, new pediatric formulations, and extended age limits for many of the studied drugs (Li et al., 2007; Rodriguez et
were submitted, the FDA approved labeling for use by that age group. The clinical review noted that “growth inhibition and hypothyroidism were two notable adverse reactions” and that they were being further evaluated in a 5-year follow-up study (Crewalk, 2008, p. 4). The review also noted that these adverse reactions presented less risk than the risk of untreated hepatitis C. The revised label included warnings about the impact of pediatric use on growth of the child.
Studies Support Different Dosing Calculation
Nevirapine (Viramune), which was first approved in 1996, was approved in 1998 for treatment of HIV infection in children ages 2 months of age to 16 years, with additional information submitted in 2002. The 2002 approval letter specifically required studies to determine dosing for younger groups. The information submitted by the sponsor in 2007 provided for dosing down to age 15 days and also provided data to support calculation of pediatric dosing based on body surface area rather than weight (Belew, 2008).
Risk-Benefit Assessment Does Not Support Pediatric Use
Omalizumab (Xolair) was approved in 2003 for treatment of moderate to severe persistent asthma in individuals 12 years of age or older. Although this approval occurred during a period when pediatric study requirements were not in effect, the FDA encouraged further pediatric studies and noted that pending legislation might require such studies (Risso, 2003). The sponsor submitted studies for the 6-to-11 age group in 2008. After the data were reviewed by FDA staff and considered in a meeting of the joint Pulmonary-Allergy, Pediatric, and Drug Safety and Risk Management Advisory Committee, the product’s labeling was revised to include the statement “Considering the risk of anaphylaxis and malignancy seen in Xolair-treated patients ≥12 years old and the modest efficacy of Xolair in the pivotal pediatric study, the risk-benefit assessment does not support the use of Xolair in patients 6 to <12 years of age” (Genentech, 2010; Starke, 2009).
SOURCE: IOM, 2012c.
al., 2008). It is probable that patent exclusivity in cancer would lead to a similar increase in research conducted in older adults and individuals with multiple comorbidities, and to an increase in knowledge about how to treat this population. Thus, the committee recommends that Congress amend patent law to provide patent extensions of up to 6 months for companies that conduct clinical trials of new cancer treatments in older adults or patients with multiple comorbidities (Recommendation 5).
The committee is concerned about some of the known limitations of the patent extension program in pediatrics, but believes the need for more
data in older adults with cancer and individuals with multiple comorbidities is so great that it justifies modeling this program in drugs used to treat older adults with cancer and individuals with multiple comorbidities. As described in the section on “How the Evidence Base for Cancer Care Decisions Is Generated,” FDA registration trials are conducted for the narrow goal of bringing new treatments to the market. Alternative strategies that mandate the inclusion of older adults and patients with multiple comorbidities in FDA registration trials have serious limitations. Such a mandate could make it more challenging to determine the efficacy and safety of a new treatment. This could make drug development more expensive, potentially require larger trials, and delay or prevent new drugs from entering the market.
Some of the main criticisms of the pediatric exclusivity provisions are briefly summarized here. A recent review of the pediatric exclusivity provision noted that it is difficult to measure any improvements in children’s health care that have resulted from the program (Kesselheim, 2011). The research conducted for the purpose of achieving a pediatric extension often has serious methodological limitations, including the only rare inclusion of drugs most frequently used by children. Most of the studies are conducted in populations of older pediatric patients (not children under the age of 6 or 2), and often at sites outside of the United States (Boots et al., 2007; Grieve et al., 2005; Pasquali et al., 2010).
The results of the research are often unpublished, and thus, not subject to peer review (Benjamin et al., 2009). When the research is published, it often focuses on findings substantively different from those highlighted in the FDA reviews and labeling changes (Benjamin et al., 2008, 2009).
Additionally, society has borne substantial costs from the delayed entry of less expensive generic versions of a drug onto the market. In a 2001 report to Congress, the FDA estimated the 20-year cost to consumers of the pediatric exclusivity to be $13.9 billion (FDA, 2001). A more recent study estimated the potential impact of the program on the U.S. Medicaid population across three classes of drugs (statins, angiotensin-converting-enzyme inhibitors, and selective serotonin reuptake inhibitors) to be $430 million over 18 months (Nelson et al., 2011). The high cost of patent extension is of particular concern when the higher drug prices are passed on to patients, because this could lead to reduced access and worse medication adherence during the extra 6 months of elevated prices (Kesselheim, 2011).
Due to the high price tag, the program has been criticized for overcompensating manufacturers (Kesselheim, 2011). The median cost of conducting clinical trials under this program was more than $12 million between 2002 and 2004, and the median net economic benefit to manufacturers was more than $134 million (Li et al., 2007). Another study found
the ratio of net economic return to cost was 17 to 1 (Baker-Smith et al., 2008). Some of the limitations, however, may be preventable in a geriatric oncology exclusivity program by having stringent requirements on the types of clinical trials that qualify for market exclusivity.
Comparative Effectiveness Research
The need to include older adults and individuals with multiple comorbidities in CER conducted by the NCI’s NCTN and others is as pressing as the need to study this population in regulatory trials. A systematic review of 345 phase 3 trials conducted by five NCI Cooperative Groups found that 57 percent of trials had no stratification by age and only 12 percent of studies had stratification of age greater than 65 years. Only one of the 345 studies was conducted exclusively in older adults (Kumar et al., 2007).
In another analysis of NCI-sponsored clinical trials between 1997 and 2000, 32 percent of the participants in phase 2 and 3 clinical trials were older adults, compared with 61 percent of individuals with new cancer diagnoses in the United States (Lewis et al., 2003). A study looking at SWOG (formerly the Southwest Oncology Group) treatment trials between 1993 and 1996 found that 25 percent of clinical trial participants were 65 years and older versus 63 percent of the overall population with cancer (Hutchins et al., 1999). Researchers’ inclusion of individuals with comorbidities in clinical research is equally poor, despite the fact that many patients have comorbidities (Alecxih et al., 2010; Dhruva and Redberg, 2008; Tinetti and Studenski, 2011; Van Spall et al., 2007).
It is unclear if CER supported by other funders does better than the Cooperative Groups at including study populations in clinical research that are representative of the majority population who actually contract the disease being studied. AHRQ has identified older adults and individuals with special health needs (e.g., chronic illness, disabilities, and end-of-life care needs) as priority populations, but no analysis has been conducted to assess whether its research includes representative populations of older adults and individuals with comorbidities (AHRQ, 2011). Similarly, it is too early to determine the impact of PCORI-funded studies on the inclusion of older adults and individuals with comorbidities in research.
Thus, the committee recommends that the NCI, AHRQ, PCORI, and other CER funders 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 distribution and health risk profile of patients with the disease (Recommendation 5). This re-
search should evaluate the efficacy, effectiveness, and toxicity of cancer interventions in these populations.
Researchers often primarily analyze only very narrow outcomes in clinical trials (e.g., progression-free survival, overall survival, toxicity) (Meropol, 2012). If the goal of clinical research is to improve the quality of cancer care, it is important to produce some of the types of evidence that would be most useful to patients and clinicians when making treatment decisions. For example, patients often want information about the estimated impact of a treatment regimen on their quality of life, functional status, symptoms, and overall experience with the disease, as well as information about other contextual factors (socioeconomic status, literacy, numeracy, language, culture, education, transportation, social supports, neighborhood, behavioral health, housing, functional and cognitive impairment, family capacity).
The PCORI methodology standards direct researchers to measure outcomes that patients “notice and care about;” however, there is currently a lack of consensus about which data are central to reaching this goal (Miriovsky et al., 2012; PCORI, 2012b). Researchers can use certain behavioral and patient data to make new discoveries regarding the benefits and harms of different treatments.
Because of the potential advantages of collecting a broader set of data during clinical trials to improve the quality of cancer care, the committee recommends that the NCI build on ongoing efforts and work with other federal agencies, PCORI, clinical and health services researchers, clinicians, and patients to develop a common set of data elements that captures patient-reported outcomes (PROs), relevant patient characteristics, and health behaviors that researchers should collect in randomized clinical trials and observational studies (Recommendation 6). The NCI could draw heavily on existing standardized formats for collecting data under many of the elements in national health population surveys (e.g., National Health Interview Survey, Behavioral Risk Factor Surveillance System) and in the NIH Toolbox, or develop new standards for use in cancer clinical trials (Ganz, 2012; NIH, 2012).
The committee recognizes that excessive data collection can reduce the overall quality of the data and increase the cost and duration of research, and that the added administrative burden can lead to reluctance by clinicians to participate in clinical research (Abrams et al., 2010; IOM, 2010b). However, the added benefits of collecting a broader set of data points during clinical research outweigh these drawbacks. Each data type that should be included in this broad set is discussed in the following
sections: PROs, biomarkers, patient characteristics, behaviors, and cost. The challenge of standardizing data collected in electronic health records is discussed in Chapter 6.
PROs can be defined as “any report of the status of a patient’s health condition that comes directly from the patient, without interpretation of the patient’s response by a clinician or anyone else” (FDA, 2009, p. 2). A PRO is measured using a self-report or an interview (if the interviewer records only the patient’s response). PROs can include severity of symptoms, quality of life, functional status, adverse events, the stages of a disease, contextual factors, and other outcomes.
Evidence shows that cancer patients are capable and willing to self-report adverse events, and clinicians accept this information in the treatment decision-making process (Trotti et al., 2007). PROs are important because the outcomes that patients’ report can be different from those collected by health care clinicians and researchers (Basch et al., 2006; Fromme et al., 2004). PROs provide additional information about treatment side effects and outcomes that are important to patients and can inform health care treatment decisions. They could be used to assess whether the cancer care delivery system is providing care that is concordant with patients’ needs, preferences, and goals, as well as to assess the impact of providing a type of care on the quality and cost of care. They also have the potential to improve patient safety in clinical studies by identifying adverse events and outcomes that otherwise would go undetected (Basch et al., 2013).
A study that compared patients and clinicians’ reporting across eight symptoms using a validated instrument found that clinicians failed to report about one-half of the symptoms identified by patients as adverse events. Similarly, the patients did not identify approximately one-half of the adverse events reported by the clinicians. The authors concluded that the clinicians’ sensitivity and specificity in reporting adverse events of common chemotherapy are limited (Fromme et al., 2004).
The importance of PROs is widely accepted in the regulatory context. The FDA and the European Medicines Agency accept the approval of drugs with labeling claims based on PROs as endpoints of safety and efficacy. In 2006, the FDA issued guidelines on using PRO measures to support labeling claims (FDA, 2006) and published the Final PRO Guidance document in 2009 (FDA, 2009). The guidance states that PRO instruments must be based on an appropriate and clearly defined conceptual framework, which requires patient interviews, focus groups, literature reviews, and expert opinion.
The majority of adverse events that currently appear on medication
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 experience (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 myelofibrosis. Incyte Corporation measured patients’ night sweats, itching, abdominal 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 development (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, interpreted, 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 Measurement Information System (PROMIS), which is a set of measures that capture 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 elements 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 increasingly recognized that patient characteristics and behaviors have an impact on cancer outcomes and will play an important role in personalized
cancer treatment (Antoni et al., 2006; Goodwin et al., 2010). Examples of characteristics that impact patient outcomes in cancer include demographics (e.g., age, sex, race/ethnicity, marital status, education); individual 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, response 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 participants’ 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 assessments in Chapter 2) (Extermann and Hurria, 2007; Extermann et al., 2012; Hurria et al., 2011).
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 during 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 difficult for patients to take the cost of care into account in their medical decision-making process (see Chapter 3).
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-
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 clinicians (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 learning health care system for cancer, which is an IT system that continually and automatically collects and compiles information from clinical practice, 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 cancer 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 researchers to use data from electronic health records (EHRs), the SEERMedicare 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, geography, ethnicity, age, education, and socioeconomic status), which could lead to the identification of subgroup variations. This would be particularly helpful in studying older adults with cancer because the learning health care system would include data on individuals with multiple comorbidities, 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;
Etheredge, 2010; IOM, 2010a, 2012a,b). It would also provide information on quality of life and functional status, which would be important to patients’ 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 interventions 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.
Because a high-quality cancer care delivery system uses results from scientific research, such as clinical trials and CER, to inform medical decisions, 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 deficits) 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.
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:
• The National Cancer Institute, the Agency for Healthcare Research 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 distribution and health risk profile of patients with the disease.
• Congress should amend patent law to provide patent extensions 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:
• The National Cancer Institute should build on ongoing efforts and work with other federal agencies, the Patient-Centered Outcomes Research Institute, clinical and health services researchers, 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 observational studies.
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 Oncology 28(27):4268-4274.
Abrams, J., R. Erwin, G. Fyfe, and R. L. Schilsky. 2010. Data submission standards and evidence 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).
———. 2013a. Who is involved in the Effective Health Care Program. http://www.effectivehealthcare.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.effectivehealthcare.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 community 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 Oncology 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 performance measures. Journal of the American Medical Association 310(2):139-140.
Belew, Y. 2008. Clinical review for Viramune (nevirapine). NDA 20636/20933. June 21. Silver Spring, MD: Food and Drug Administration. http://www.fda.gov/downloads/Drugs/DevelopmentApprovalProcess/DevelopmentResources/ucm072777.pdf (accessed 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 European 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 intersection 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.
Cox, K., and J. McGarry. 2003. Why patients don’t take part in cancer clinical trials: An overview 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. December 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 recommendations 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/GuidanceComplianceRegulatoryInformation/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/GuidanceComplianceRegulatoryInformation/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/developmentresources/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 product 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).
———. 2012c. Guidance for industry. E7 studies in support of special populations: Geriatrics. Questions and answers. http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/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/drugsatfda_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 Oncology 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 effectiveness 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 (workshop 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. Underrepresentation 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 summary. 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.
———. 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. Washington, 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. Washington, 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 prospective 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 development: 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.
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 information technology, electronic health records, and continuously aggregating data to comparative 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 terminology criteria for adverse events (pro-ctcae). http://outcomes.cancer.gov/tools/proctcae_fact_sheet.pdf (accessed March, 2012).
———. 2012b. Prioritization/scientific quality initiatives. Place Published. http://transformingtrials.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/noticefiles/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. Globalization 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.
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 initiatives: 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 Administration. 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 recruitment 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 sampling 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. Systematic 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.