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Comparative Effectiveness
Research: What It Means for
Radiology1
Pari V. Pandharipande, MD, MPH
G. Scott Gazelle, MD, MPH, PhD
Published online
10.1148/radiol.2533091286
Radiology 2009; 253:600 – 605
1
From the Department of Radiology, Institute for Technology Assessment, Massachusetts General Hospital, 101
Merrimac St, 10th Floor, Boston, MA 02114. Received
July 16, 2009; revision requested August 13; revision
received August 25; final version accepted August 25.
Address correspondence to P.V.P. (e-mail: pari@mgh
-ita.org).
Authors stated no financial relationship to disclose.
姝 RSNA, 2009
600
D
iagnostic imaging is an important
and costly component of medical
care. In 2007, Medicare expenditures for imaging services totaled $11.4 billion; this represented a substantial increase
from the $8.4 billion spent in 2002 (1). As
the debate about health care reform has
accelerated, further attention has been focused on the need to carefully assess the
benefits and costs of imaging. In particular,
there has been concern that expensive imaging tests often have been ordered and
performed without sufficient evidence to
support their appropriateness or potential
benefits (2,3). For example, in a recent article in the New England Journal of Medicine, Avorn argued that currently, there is
“almost no evidentiary bar at all” to gain
Food and Drug Administration approval of
imaging technologies (4).
The problem is not inattention to research: In the past 2 decades, advances in
radiology have soared, due at least in part
to an ever-expanding imaging research enterprise. However, studies in which researchers quantify the long-term effects of
imaging on patient outcome remain sparse.
A few challenges may explain this evidence
gap (5). First, diagnostic test results are
intermediate outcomes; they influence, but
do not directly determine, patients’ health
outcomes. Factors such as disease severity,
treatment effectiveness, or system performance may modulate the effect of imaging
on outcomes. Second, imaging technology
is a moving target; techniques are developed, refined, and become obsolete
quickly. This makes it difficult to conduct
sufficiently large trials quickly enough to be
relevant. Third, randomized controlled trials of imaging technologies present several
challenges. Because the majority of diagnostic imaging tests have minimal or no adverse effects and because of the general
perception among patients and physicians
that more information leads to better care,
it is often both practically and ethically difficult to withhold a diagnostic test from patients enrolled in a randomized trial.
Furthermore, it is often the case that several tests or combinations of tests are potentially useful; however, comparing all
of these tests with sufficient statistical
power to detect meaningful differences in
outcomes may require an extremely large
trial that could take years to complete and
be prohibitively costly. Even if such trials
could be completed, the results may be confounded by advances in treatments, imaging technologies, or both.
There has been no better time to address these challenges than now. The
American Reinvestment and Recovery Act
has recently placed patient-centered outcomes research, termed comparative effectiveness research (CER), at the forefront of
funding priorities (3,4,6–12). In the next 2
years alone, $1.1 billion will be allocated
specifically for CER and will be divided
among the National Institutes of Health, the
Agency for Healthcare Research and Quality, and the Office of the Secretary in the
U.S. Department of Health and Human
Services (6,7,11). Additional funds may be
devoted to CER through the Challenge
(RC1) and Grand Opportunity (RC2) grant
mechanisms. The coming years will be an
ideal time for investigators in radiology to
develop a CER initiative and establish a lasting precedent for CER in our field.
In this article, we aim to (a) define
CER, with a focus on its application to radiology; (b) describe research activities that
fall under the CER umbrella; (c) present
CER topics in radiology that will receive
priority for funding; (d) describe the current role of cost-effectiveness analysis in
CER; and (e) encourage changes within our
field that will support CER.
Defining CER from the Radiologist’s
Standpoint
The American Reinvestment and Recovery Act established the Federal Coordinating Council for Comparative Effectiveness Research to direct the vision and
priorities for CER conducted under all
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OPINION: Comparative Effectiveness Research
federal agencies. On June 30, 2009, this
council submitted a publicly available document to the President and Congress in
which they defined CER and outlined
their mission for the upcoming years (7).
CER was defined as:
... the conduct and synthesis of research comparing the benefits and
harms of different interventions and
strategies to prevent, diagnose, treat
and monitor health conditions in
“real world” settings. The purpose of
this research is to improve health
outcomes by developing and disseminating evidence-based information to
patients, clinicians, and other decisionmakers, responding to their expressed
needs, about which interventions are
most effective for which patients under
specific circumstances. (7)
The central goal of CER is to fill gaps in
knowledge that add uncertainty to everyday medical decision making (6,7,11).
One may ask, “Isn’t that the goal of most
clinical research?” The primary difference is that the knowledge gained from
CER should, in a transparent way, inform
and improve health care decisions when
multiple treatment options are available
(6,7,11). A key component of CER is the
identification and comparison of all relevant alternatives; in many settings, this
precludes comparison with a placebo
group or with a group that did not undergo treatment. Although CER has not
been a central focus of imaging research
to date, over the past 2 decades, CER
activities have represented a growing minority of research initiatives in radiology
(13–16). In this article, we will provide
examples of published CER investigations
in radiology and describe their potential
roles in patient and policy decisions.
A Patient’s Decision: Breast Cancer
Screening in a BRCA1 Mutation Carrier
Consider a BRCA1 mutation carrier who
must decide how to manage her increased risk for breast cancer. If she opts
for screening with imaging, she would like
to know the relative benefits of mammography alone versus mammography combined with magnetic resonance (MR) imaging. MR imaging strategies are more
sensitive but less specific in the detection
Pandharipande and Gazelle
of breast cancer than are mammographic
strategies (17). However, sensitivity and
specificity, in isolation, provide insufficient information concerning the longterm implications of choosing one strategy over another.
Investigators have used computer
modeling techniques to simulate the natural history of breast cancer in high-risk
patients and then compared MR imaging
and mammographic screening strategies
in hypothetical high-risk populations
(17,18). In so doing, they have estimated
the life expectancy benefits of combined
MR imaging and mammography over
mammography alone (17,18). These
studies show one application of CER in
radiology and its potential value in a realworld setting.
A Policymaker’s Decision: Funding CT
Screening Programs for Smokers
Consider a policymaker who is deciding
whether to fund chest computed tomographic (CT) screening for smokers. By
screening smokers with chest CT, it is
possible to more than double the number
of early-stage lung cancers detected (19–
24). Furthermore, one group of investigators reported a 10-year survival rate of
more than 80% for patients with screendetected lung cancers in a single-arm
study; this would represent a large potential improvement over the expected survival of patients with symptom-detected
cancer (19). Nevertheless, these data are
insufficient to enable the policymaker to
make an informed decision. Biases such
as lead time, length, and overdiagnosis
could account for the reported positive
results of single-arm screening studies
and negate any predicted benefits of
screening.
Large randomized controlled trials
performed to assess the benefit of CT
screening in smokers are underway
(25,26). In the interim, computer modeling techniques have been used to simulate
comparative trials and quantify the mortality reduction, if any, that screening
may confer (27–29). In these studies, investigators have estimated and accounted
for the previously mentioned biases and
have incorporated additional factors,
such as the competing mortality risks in
smokers and the sequelae of false-positive
Radiology: Volume 253: Number 3—December 2009 ▪ radiology.rsna.org
CT results (27–29). Mortality reductions
conferred by screening were found to be
modest or nonexistent (27–29). Until randomized controlled trial results become
available, a policymaker could use results
from CER modeling studies to estimate
whether the predicted benefits of a CT
screening program justify its cost or
whether available resources might be better spent on a different public health program.
It is important to note that computer
modeling techniques are one way of conducting CER, but they are by no means
the only way to conduct CER. In imaging
research, when clinical trials are impossible or impractical to conduct, computer
models can serve as powerful tools with
which to predict the effects of imaging
performance on long-term patient outcomes (5). Increasingly sophisticated
modeling and model calibration techniques have resulted in models for a number of diseases that are highly accurate
and useful (30). However, computer
models are limited in that they cannot
reproduce every facet of a complex disease process; therefore, they should be
viewed as complementary to other research approaches. A variety of methods
can and should be used for successful CER,
as will be detailed later in this article, and
the investigator’s research design should be
based on the clinical question at hand.
Research Activities under the CER
Umbrella
When surveying the literature on
CER, it becomes apparent that there
is no precise definition of what qualifies as CER and what does not
(6,7,11,12). In fact, CER includes
many types of investigations. Consider
a scenario in which investigators conduct decision analysis with a variety of
data sources, including meta-analysis,
published randomized controlled trials, and observational data. Suppose
the decision analysis informed a
change in health care policy. To implement this change, the investigators develop a search engine with which to
identify patients in their hospital’s database that could most benefit from
the new policy. All elements of this
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OPINION: Comparative Effectiveness Research
process— data gathering and synthesis, medical decision making, and
translation of results into practice—are
integral to CER. While the endpoint of
CER is to direct medical decisions, the
heterogeneity of approaches that can be
used to achieve this goal is well-recognized in the council’s document (7).
Thus, instead of investing in a single
type of research activity, the council’s intent
is to promote the development of CER infrastructure. Accordingly, to guide investment priorities, the council put forth a strategic framework that included the following
four core categories of activity that are essential to durable successful CER: research,
human and scientific capital, CER data infrastructure, and dissemination and translation of CER (7). The council stated that
CER may encompass more than one of
these categories when addressing a
broader agenda and specifically put
forth three cross-cutting themes—
priority populations, conditions, and
interventions—to encourage a multipronged approach to their study (7).
The core CER activities and crosscutting themes comprise the council’s
strategic framework (7).
Here, we focus on defining the core
categories of CER activity. Research re-
Pandharipande and Gazelle
fers to hypothesis-driven patient-oriented
investigations, such as studies in which
researchers compare different imaging
strategies. Human and scientific capital
refers to educating investigators, particularly new investigators, in CER methods
and to promoting innovations in CER
methods. CER data infrastructure refers
to the development of electronic databases that can facilitate CER. In radiology, such databases ideally should integrate patient and imaging information.
Dissemination and translation of CER refers to creating decision aids, tools, and
programs to effectively implement and
disseminate CER results (7).
Of these four categories, the council’s
primary priority is the development of
CER data infrastructure, underscoring
their research engine approach (7).
Given that radiology is a field that has
been digital for 10 years or more in many
academic institutions, our field has an opportunity for leadership in this domain, as
will be discussed later in this article.
High-Priority Areas of CER in Radiology
As part of the American Reinvestment
and Recovery Act, the Institute of
Medicine was asked to develop a list of
100 top-priority areas for CER (3,6).
The Institute of Medicine also issued
its report on these topics on June 30,
2009, by quartile ranking (6). Of the
100 topics, nine were directly relevant
to radiology (Table) (6).
When reviewing these nine topics,
two themes are striking. First, the Institute of Medicine identified oncologic
radiology as the highest-priority area
for CER initiatives in radiology (3,6).
While only one imaging topic was included in the top 25, this topic—a call
for investigators to compare the effectiveness of PET, CT, and MR imaging
in all venues of cancer care— encompasses most of current cancer imaging
(3,6). The Institute of Medicine’s emphasis in the second quartile on comparing
screening strategies for breast and colorectal cancer and in the fourth quartile on
studying outcomes after different treatment choices for liver metastases (including ablative therapies) further reflects the
importance they placed on oncologic radiology (3,6).
Second, the goal of two of the nine
topics is to elucidate the role of specialists in imaging (6). In the third
quartile, one topic calls for a comparison of the effectiveness of imaging
CER Topics Relevant to Radiology Among 100 High-Priority Topics Designated by the Institute of Medicine
Quartile
First
Second
Second
Second
Third
Third
Fourth
Fourth
Fourth
Objective
Compare the effectiveness of imaging technologies in diagnosing, staging, and monitoring patients with cancer, including PET, MR imaging, and CT
Compare the effectiveness of film-screen or digital mammography alone and mammography plus MR imaging in community practice–based screening
for breast cancer in high-risk women of different ages, risk factors, and race or ethnicity
Compare the effectiveness of new screening technologies (such as fecal immunochemical tests and CT colonography) and usual care (fecal occult
blood tests and colonoscopy) in preventing colorectal cancer
Compare the effectiveness and outcomes of care with obstetric US studies and care without use of US in normal pregnancies
Compare the effectiveness of traditional risk stratification for coronary heart disease and noninvasive imaging (using coronary artery calcium, carotid
intima media thickness, and other approaches) on coronary heart disease outcomes
Compare the effectiveness of traditional and newer imaging modalities (eg, routine imaging, MR imaging, CT, and PET) when ordered for neurological
and orthopedic indications by primary care practitioners, emergency department physicians, and specialists
Compare the effectiveness of CT angiography and conventional angiography in assessing coronary stenosis in patients at moderate pretest risk of
coronary artery disease
Compare the effectiveness of diagnostic imaging performed by radiologists and nonradiologists
Compare the effectiveness of surgical resection, observation, or ablative techniques on disease-free and overall survival, tumor recurrence, quality of
life, and toxicity in patients with liver metastases
Source.—Reference 6.
Note.—Quartiles are arranged from top (first) to bottom (fourth). Topics relevant to radiology were extracted from 100 CER topics prioritized across all medical disciplines by the Institute of Medicine.
Topics within each quartile (groups of 25) were considered of equivalent priority. PET ⫽ positron emission tomography, US ⫽ ultrasonography.
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OPINION: Comparative Effectiveness Research
studies ordered by specialists versus
those ordered by nonspecialists (6). In
the fourth quartile, another topic calls
for a comparison of the effectiveness
of imaging performed by radiologists
versus that performed by nonradiologists (6). Inclusion of these topics may
reflect concern about financially motivated self-referral or skepticism regarding the value of specialization for
those performing imaging interpretations.
Is Cost-effectiveness Analysis a Part of
CER?
The question of whether economic analysis
should be included in or excluded from CER
has been the subject of recent ardent debates (5,7,10,11,31–33).
The council discusses stakeholders’ opposing opinions in their document (7). Proponents of including
cost-effectiveness analysis argue that
without it, the relative value of competing health care strategies will remain unknown (4,7,11,33). Thus, policy decisions regarding health care resource allocation will be uninformed
and, likely, inappropriate. The proponents highlight the risk of passing up
high-value care—which will improve
our society’s overall life expectancy
and quality of life at a reasonable
cost—for expensive low-yield care
(11). They argue that by excluding
economic analysis, a critical aspect of
prudent decision-making will be missing.
The Society of Medical Decision
Making, in their background paper on
CER, invoked Garber’s well-known
analogy of choosing healthcare programs at a societal level to selecting
items from a restaurant menu (11,33).
Regarding the importance of costeffectiveness analysis in CER, they
stated that “it strengthens the usefulness of CER immensely . . . otherwise,
it would be like translating the foreignlanguage menu of healthcare choices
into your own language, but then putting ‘white-out’ over the prices”
(11,33).
Stakeholders who propose to exclude cost-effectiveness analysis from
Pandharipande and Gazelle
CER are concerned that cost information may inappropriately influence research efforts and medical decision
making (4,7). Some argue that cost
information will “taint” CER, rendering simultaneous analyses of clinical
effectiveness more questionable (10).
Many argue that the results of economic analyses would indirectly and
inappropriately drive medical decisions by facilitating policies that would
restrict coverage and access to care
(4,7). Concerns have also been raised
that economic analyses would unfavorably affect physician reimbursement (7).
Perhaps as a result of these opposing viewpoints, the council does not
emphatically include or exclude costeffectiveness analysis as part of CER
(7). However, when detailing how a
CER proposal should be judged, the
council includes the potential effect of
a proposal on costs as one factor that
may be used to estimate its overall
priority (7).
Improving Care in Radiology through
CER Initiatives
To ensure that we, as radiologists, are
able to provide high-quality high-value
care for our patients, we will need to prioritize CER in the coming years. This will
be best accomplished by focusing, in the
immediate future, on developing a strong
CER infrastructure, as the council emphasized in their proposed strategic framework (7).
First, continued high-quality research
efforts, ranging from studies in which researchers compare imaging test performance characteristics to those in which
researchers compare longer-term outcomes for different imaging strategies,
are critical for CER to succeed in radiology. Such efforts should invoke a highly
interdisciplinary approach and include
collaborations with clinical colleagues and
experts in outcomes research methods.
These collaborations are essential for durable CER initiatives. Several American
College of Radiology Imaging Network
(ACRIN) trials exemplify such research
efforts. Multicenter ACRIN trials in which
researchers evaluate new techniques for
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use in cancer detection and staging span
the fields of breast (34–36), colorectal
(37), cervical (38,39), prostate (40), and
lung (25) cancer, and the results have
been widely used to inform policy and
reimbursement decisions.
Second, investments in training for
new investigators in outcomes research
methods will be essential for CER in radiology to succeed. This will necessitate increased funding for career development
awards. This will also require recruiting
trainees into outcomes research careers,
which will call on health services researchers in radiology to participate more
actively in the training of residents and
fellows.
Third, electronic databases that facilitate CER will need to be further developed and maintained. The council’s emphasis on this aspect of CER infrastructure is an advantage for the radiology
community (7). As noted earlier, many
academic institutions are just a few steps
away from being able to generate robust
electronic databases that can integrate
patient and imaging information. An example includes Render, a search application and repository developed at the institutional level that enables investigators to
search a detailed database of patient and
imaging information in an online format
(41). This type of database will also facilitate the involvement of trainees in CER:
It removes the burden of case identification, which enables trainees to focus
more time on the core elements of a given
research project. The National Oncologic
PET Registry is an example of a national
database (42). Its primary purpose is to
provide evidence for PET-related Center
for Medicare & Medicaid Services coverage decisions (42). Proposals for the development of a variety of electronic databases with which to conduct CER in radiology are likely to be prioritized in the
near future.
Fourth, development of tools and
programs to disseminate CER results in
radiology will need to receive greater emphasis. This is a challenge for CER in all
fields. An example of this type of tool
would be a Web-based algorithm in
which a patient could enter demographic,
health, and preference information to
gain knowledge about the risks and ben603
OPINION: Comparative Effectiveness Research
efits of using an imaging surveillance
strategy in cancer detection. The development of high-quality decision support
tools for patients and practitioners across
imaging fields will undoubtedly improve
the efficiency and quality of care in radiology.
Conclusion
There has been no better time than now
for radiology, as a field, to make a lasting
commitment to CER. New funding
sources available to support outcomes research provide a singular opportunity to
build the infrastructure that is needed to
conduct high-quality CER. However, simultaneous support within the radiology
community is also needed at multiple levels. Radiology departments within academic institutions will need to allocate
greater resources to CER efforts, providing start-up and ongoing support to “dry
laboratory” outcomes researchers that is
commensurate to that provided for investigators in “wet laboratory” basic science
settings. Radiologic societies will need to
augment funds allocated to train new investigators in outcomes research methods and support innovative CER proposals. The private practice community can
substantially contribute to CER by increasing contributions to radiologic societies to fund these endeavors. Demonstrable support within the radiology community, in turn, will increase the
likelihood of sustained federal CER funding and ensure that future research endeavors in radiology will include both
technology development and assessment,
without disproportionate representation
of the former. For the viability of our
field, this undertaking is not a choice but a
requirement. We have a responsibility to
provide high-value high-quality care to
our patients in a way that is as transparent as possible. In the end, that is what
our profession is all about.
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