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SCIENTIFIC PRINCIPLES
Researchers have a wide variety of comparators from which to choose when
designing randomized trials of behavioral interventions [1]. The choice is an easy
one in some cases. In a comparative effectiveness trial, for instance, the research
goal is to compare two or more existing interventions or strategies. Here, the groups
essentially choose themselves. In other cases, however, the choice can be quite dif-
cult due to long-standing uncertainties and disagreements about whether, when,
and how to use certain comparators.
These disputes are manifestations of a deeper controversy about decision- making
frameworks. There are multiple frameworks for choosing comparators. There is, to
date, no rm consensus on which one(s) to use.
Decision-Making Frameworks
Table 6.1 summarizes seven
common frameworks that are
explicitly or implicitly used to
guide decisions about the
appropriate comparator in a
behavioral trial. Each of these
frameworks is described below.
Implicit Rules
Comparator choices in behav-
ioral intervention research are
often determined by implicit
rules. Among these rules are
the following.
Maintain the Status
Quo.
If a particular compara-
tor has been used in similar
studies, then it must be the
(safe, smart, or right) choice
for (my or your) study too.
This rule assumes that the
comparator in question was
the best choice for the previous
studies even though it might not have been, and it privileges conformity over logic.
Table 6.1 Decision-making frameworks
Source of Guidance Key Attributes
Implicit rules Offers tacit guidance, absorbed
from and reinforced by cultural
environment
Explicit rules Offers more transparent
guidance, but may be
excessively rigid
Quality-guided
decisions
Aims to ensure that the trial’s
methodology will be judged
favorably
Phase-guided
decisions
Recommends different
comparators for different phases
of intervention research
Purpose-guided
decisions
Asserts that the trial’s primary
purpose outweighs other
considerations
Context-guided
decisions
Recognizes constraints on
comparator choices imposed by
contextual factors
Evidence-based
decisions
Provides empirical data that can
either support or challenge any
of the above
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Tougher is Always Better. This rule implies that comparators that are rela-
tively hard for interventions to outperform are inherently better than ones that are
relatively easy to outperform. It derives from widespread dissatisfaction with the
laxity of no-treatment and wait-list comparators [2, 3], a belief that placebo effects
play problematic roles in psychotherapeutic and other behavioral interventions [4],
and a desire to emulate the rigorous, double-blind, placebo-controlled designs that
are used in drug research [5]. It would be easier to choose comparators for behav-
ioral interventions if “tougher” were always better, but sometimes it is not, for rea-
sons to be discussed later in this chapter.
Implicit rules can be learned without ever having been articulated overtly. One can
follow them without having been told to do so and without being aware of doing so
[6]. Implicit rules facilitate decision-making under uncertainty. To the extent that
they seem self-evident, they relieve us from any burden of proof. Holding the same
ones as our mentors and peers also gives us an entrée to a shared culture and a sense
of belonging. These factors can make implicit rules easy to adopt, easy to follow,
and hard to abandon even when confronted with evidence that contradicts them.
When different researchers inculcate different implicit rules, they tend to disagree
about comparator choices even if their respective positions are not very well dened
or supported. Such disputes can be difcult to resolve and slow the progress of
behavioral intervention research when they adversely inuence the fate of grants
and papers.
Explicit Rules
Explicit rules for behavioral trials that would mandate or prohibit the use of various
comparators could resolve disputes created by implicit rules but create other
problems.
The most restrictive rule would mandate a single, one-size-ts-all comparator for
behavioral trials. Drug research is one of the inspirations for this idea, as it is for the
belief that tougher is always better. It posits that because most drug trials are pla-
cebo-controlled, the best comparator for behavioral research would be an all-
purpose placebo condition. A less restrictive version recognizes that different
circumstances may call for different comparators, but holds that explicit rules
should determine the choice. Proponents of this position point to the fact that the
Food and Drug Administration (FDA) regulates the design of pharmaceutical trials
and that this helps to make drug research orderly and predictable.
In reality, the FDA regulations provide exible guidelines for comparator choices
rather than mandatory rules or standards. The “Adequate and Well-Controlled
Studies” section of the Code of Federal Regulations (21CFR314.126) states that
ve different comparators are generally recognized in drug trials (see box). The
FDA Drug Study Designs Information Sheet states that these comparators “can be
useful in particular circumstances” and that “no general preference is expressed for
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any one type, but the study design
chosen must be adequate to the
task” [7]. It also discusses the
advantages and disadvantages of
these comparators and the usual
circumstances in which each one
may be used. In short, the FDA
offers guidance instead of dictat-
ing comparator choices.
Nevertheless, the FDA is more likely to accept evidence from trials that are respon-
sive to their guidelines than evidence from other trials.
Quality-Guided Decisions
The FDA is not in the business of approving behavioral interventions, so behavioral
trials are not designed with FDA approval in mind. However, other kinds of approval
may be sought for behavioral interventions, such as recommendations in evidence-
based clinical practice guidelines. Whether trials are included in high-impact meta-
analyses and clinical practice guidelines depends on whether they are
methodologically rigorous according to research quality measures such as the
Cochrane Collaboration’s tool for assessing risk of bias in randomized controlled
trials [8]. Consequently, researchers have incentives to design their trials in ways
that merit high ratings on such measures.
The Cochrane risk of bias tool lists a number of specic methodological decien-
cies that could cause a behavioral trial to be classied as having a high risk of bias,
such as the use of an inadequate randomization procedure. However, it does not
include any specic criteria for the choice of comparators. Neither does the Grading
of Recommendations Assessment, Development, and Evaluation (GRADE) system
of rating the quality of evidence [9]. Although quality of evidence rating measures
may be useful guides for many different behavioral trial design decisions, they offer
little guidance for choosing comparators.
Phase-Guided Decisions
A seminal paper by Mohr etal. [10] explains why different comparators may be
needed in different phases of behavioral intervention development. Early trials of
novel behavioral interventions tend to be small and to ask whether the treatment has
any detectable effects. Type II errors (i.e., failing to nd a true benet) can have
more serious consequences than Type I errors (i.e., nding a spurious benet) in
early-phase research because they can thwart continued development of promising
interventions and/or promote premature abandonment of them. This favors the use
of relatively lenient comparators such as wait-list or no-treatment conditions in
early-phase trials. In contrast, later-phase trials tend to be larger and to ask whether
the treatment has robust and clinically meaningful effects. The dominant concerns
in this phase of research shift to avoiding Type I errors to maximize the clinical
implications of the trial. These considerations favor more formidable and clinically
relevant comparators.
FDA’s “Recognized” Comparators
Placebo concurrent comparator
Dose-comparison concurrent comparator
No treatment concurrent comparator
Active treatment concurrent comparator
Historical comparator
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Mohr etal. [10] argue that the comparators that best serve the needs of the ultimate
stakeholders in an intervention (including patients and providers) also shift as indi-
vidual research programs progress from Phase I studies through Phase III and IV
trials. Different comparators may be needed for early investigations of an interven-
tion than for trials conducted when understanding of the intervention is more
mature. For example, Bower [11] recently suggested that research on mindfulness
interventions for cancer survivors has advanced to a point at which wait-list com-
parators have outlived their usefulness and more formidable and clinically relevant
comparators are needed.
Purpose-Guided Decisions
Purpose-Guided Trial Design (PGTD) [12] is a heuristic framework for choosing
comparators for behavioral trials. Its fundamental principle is that the design of a
behavioral trial should be guided by its primary purpose. A unique feature of PGTD
is that it recognizes the existence of conicting demands in the trial design process
and asserts that compatibility of the design with the primary purpose of the trial
outweighs any conicting methodological considerations or secondary aims. The
primary purpose is the main justication for conducting the trial. If it is sacriced
for the sake of less important considerations, the trial loses its raison d’être.
For example, the primary purpose of a trial may be to determine whether a behav-
ioral intervention that requires multiple therapeutic contacts produces better out-
comes than are typically obtained in routine clinical practice settings where such an
intervention is not available. The comparator that would best serve the primary pur-
pose of such a trial might be called usual care (UC), treatment as usual (TAU), or
standard care, depending upon the area of research. For simplicity, it will be called
usual care.
Although UC would t the primary purpose of the trial, it would also have limita-
tions. An obvious one is that the UC participants would not have any contacts with
a trial therapist, whereas those in the intervention arm would have multiple contacts.
Consequently, exposure to the intervention will be confounded with contact time. If
the outcomes are signicantly better in the intervention than the UC arm, it could be
argued that this does not constitute evidence that the experimental intervention is
efcacious. It is possible that simply spending the same amount of time with the
participants, minus the elaborate intervention, might yield similar benets.
This criticism focuses on a possible rival explanation for the apparent effects of the
intervention. Comparators can be designed to rule out various rival explanations. In
this example, the investigator could have chosen a comparator that provides the
same amount of contact with a therapist that the intervention provides. In doing so,
however, they would have created a comparator that is decidedly unlike routine
clinical care. Hence the trial would not answer the key question of whether the
intervention is superior to routine care.
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PGTD acknowledges the importance of threats to internal validity and other rival
explanations for treatment effects, but places highest priority on compatibility with
the primary purpose of the trial, even if this makes it difcult to rule out certain rival
explanations for the ndings. This is not an excuse for methodological weaknesses
or a way to downplay internal validity. It simply acknowledges that the trial design
process often involves trade-offs, and, when it does, the primary purpose of the trial
should take precedence over other concerns. If a trial does not serve its primary
purpose, it is not worth conducting.
Context-Guided Decisions
The context within which a behavioral trial is conducted can impose constraints on
comparator choices. For example, if a trial with a “no-treatment” comparator
focuses on a disorder that is treated routinely in clinical practice, some of the par-
ticipants may receive treatment for the disorder outside of the trial. This could occur
even if the investigator were to urge the participants to refrain from seeking any
non-trial care for the disorder during the course of the trial. Thus, even if the inves-
tigator were to call the comparator a “no-treatment” group, contamination by non-
trial care would effectively turn it into a UC condition [1].
Evidence-Based Decisions
A small but growing empirical literature on comparators is challenging some long-
held beliefs and may help to guide the selection, design, and implementation of
comparators for future behavioral trials. For example, formidability (i.e., the
expected amount of pre-post change on the primary outcome measure) is one of the
most important considerations in choosing among comparators. Certain compara-
tors are expected to be more formidable (i.e., more likely to produce change) than
others. All else being equal, many investigators would expect to see more pre-post
improvement in participants who are randomly assigned to TAU than to a wait-list
condition.
However, Mohr etal. [13] performed a meta-analysis of a number of comparator
features in trials of psychotherapy for depression. One of their analyses showed,
contrary to expectation, that comparisons of depression interventions to wait-list
control conditions yielded effect sizes that were not signicantly larger than com-
parisons between depression interventions and TAU.This suggests that the formi-
dability of TAU and wait-list comparators may be more similar in depression
treatment trials than was previously believed. The formidability of the comparator
is an important consideration in designing behavioral trials, and ndings such as
the ones reported by Mohr etal. are not only raising questions about reliance on
traditional beliefs about comparator formidability but also fostering empirically
informed design decisions. Feasibility pilot studies that test the formidability of a
comparator on primary and secondary outcomes for a planned trial is an underuti-
lized, but nonetheless extremely valuable, part of preparation for Phase II and
Phase III efcacy trials.
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CHALLENGES FOR
BEHAVIORAL TRIALS
Historical Perspective
The pros and cons of
various comparators
have been debated ever
since the dawn of psy-
chotherapy research
[
14]. The discussion
began in 1936 when the
eminent psychologist,
Saul Rosenzweig,
argued that despite
claims to the contrary by
proponents of particular
brands of therapy, all
forms of psychotherapy
have similar effects and
common factors, such as
clinical attention, which
account for a substantial
proportion of their ef-
cacy [15]. He likened
this to the scene in
Alice’s Adventures in
Wonderland by Lewis
Carroll wherein a dodo bird organized a race in which everyone was allowed to start
and stop whenever they liked. At the end of the race, the dodo bird declared,
“Everybody has won, and all must have prizes.”
Rosenzweig’s proposition, which came to be known as the “Dodo Bird Conjecture,”
gained empirical support with the 1997 publication of an inuential meta- analysis of
research on “bona de” psychotherapies [16]. The meta-analysis was limited to trials
that had directly compared two (or more) different forms of psychotherapy. It was
found that the effect sizes were homogeneously distributed around zero, consistent
with the Dodo Bird Conjecture. There is evidence to the contrary, but it is equivocal.
Even if some psychotherapeutic interventions are superior to others for certain disor-
ders, common factors are important components of all of them.
Something resembling the dodo bird shows up in many other areas of behavioral
intervention research, beyond the traditional terrain of psychotherapy. For example,
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a widely publicized meta-analysis found minimal differences in efcacy among a
variety of popular weight loss diets, but all of them achieved a common factor of
reduction in calories [17]. Novel interventions often generate a lot of enthusiasm,
but the sobering reality is that dramatic breakthroughs are rare in behavioral inter-
vention research. Incremental advances tend to be a more realistic goal.
Numerous behavioral trials have compared various psychotherapeutic or other
behavioral interventions to no-treatment or wait-list comparator groups [18]. These
trials have been criticized for failing to control for common factors and for being too
lenient. This has led some researchers to conclude that trials of psychotherapeutic
and other behavioral interventions should always include a common factor com-
parator. This belief is reinforced by the fact that most standard drug trials are
placebo- controlled. Despite the appeal of this response to the Dodo Bird Conjecture,
there are circumstances in which common factor comparators create more problems
than they solve.
The Status Quo
There has never been a consensus about which framework(s) should guide the selec-
tion of comparators for behavioral trials. Starting from different premises and rely-
ing on different frameworks, different researchers often reach different conclusions
about which comparator should be used in any given trial. The present situation is
reminiscent of Akira Kurosawa’s classic 1950 lm Rashomon, in which a number of
witnesses to a crime contradict each other’s versions of the same incident.
This is problematic for individual grant applicants whose comparator choices may
nd disfavor among reviewers and for authors of clinical trial reports whose com-
parator choices may be criticized when it is far too late to do anything about them.
It is also problematic for the entire eld of behavioral clinical trials. Consequently,
many researchers are looking for some sort of consensus to emerge and wondering
whether a new framework will prevail.
OVERCOMING THESE
CHALLENGES IN A
BEHAVIORAL TRIAL
There are two emerging frameworks that provide guidance on the choice of an
appropriate comparator in a behavioral trial. These frameworks release the investi-
gator from implicit rules and facilitate reasoned decision-making.
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The Pragmatic Model forComparator Selection
inHealth-
Related Behavioral Trials
The Ofce of Behavioral and Social Sciences Research (OBSSR) in the National
Institutes of Health recently responded to the unsatisfactory state of affairs in choos-
ing a comparator for a behavioral trial by convening an expert panel on the selection
of comparators for trials of behavioral interventions. They asked the panel to
develop a set of recommendations on how to decide on the optimal comparator and
publish them in 2019. After reviewing existing frameworks (discussed above), the
panel developed a decision-making approach called the Pragmatic Model for
Comparator Selection in Health-Related Behavioral Trials [19]. This model advo-
cates decision- making in six steps (Fig.6.1).
(A)
Position the trial within an applicable
framework (purpose, phase, and/or context)
(B)
Define primary purpose of the trial
(primary objective, research question,
and/or hypothesis)
(C)
Choose the optimal comparator
for the primary purpose of the trial
Insurmountable Surmountable
Absent
Absent
Present
Modify
comparator
Reconsider
feasibility
and purpose
(D)
Significant
Barriers
(E)
Significant
Limitations
(F)
Finalize choice of optimal comparator
Acknowledg
e
limitations,
ameliorate
if possible,
address in
future studie
s
Fig. 6.1 The Pragmatic Model for Comparator Selection in Health-Related Behavioral Trials
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Relevant Framework (A)
Step A positions the trial within an applicable translational research framework,
such as the ORBIT model [20]. Showing how and where the trial ts into a sequence
of studies or a program of translational research claries its purpose. It enables the
investigator to differentiate between aims that will be addressed in the present trial
and those that have already been addressed in previous studies or that will be
addressed in studies planned for the future.
Primary Purpose oftheTrial (B)
Step B denes the primary purpose of the trial. Like the Purpose-Guided Trial
Design Framework (see below), the Pragmatic Model asserts that the optimal com-
parator for any behavioral trial is the one that best serves the trial’s primary purpose.
It recommends use of the Population, Intervention, Comparator, Outcome, and
Timing (PICOT) format [21] to clarify the purpose.
Optimal Comparator (C)
Step C species the optimal comparator in light of the phase of the research and the
primary question to be answered within that phase. One of the distinctive features
of the Pragmatic Model is that it doesn’t end with the identication of the optimal
comparator that best ts the primary purpose of the trial.
Signicant Barriers (D)
Step D considers signicant barriers to the choice of the optimal comparator.
Common barriers include ethical concerns, stakeholder objections, and practical
considerations such as excessive costs. For example, a placebo condition might be
the optimal comparator for a hypothesis that an investigator wants to test about a
treatment for depression, but the investigator’s institutional review board might rule
that it would be unethical to randomly assign depressed participants to a placebo
condition. As another example, wait-list comparators are usually more expensive to
implement than no-treatment conditions, since the former involves administering an
intervention after a waiting period, whereas the latter does not. If a trial’s budget is
large enough to cover a no-treatment comparator, but not a wait-list comparator,
then the latter would not be feasible.
When confronted with a barrier to the use of the optimal comparator, the investiga-
tor’s task is to determine whether the barrier is surmountable or insurmountable. If
a minor modication of the comparator would overcome the barrier, then modica-
tion is probably the best approach. If the barrier is insurmountable, it may not be
possible to test the hypothesis, and the investigator may have to reconsider the pur-
pose and feasibility of the trial. The Pragmatic Model cautions against switching to
an acceptable and feasible, but suboptimal, comparator if there is an insurmountable
barrier to using the optimal comparator. This misguided strategy would lead the
investigator to conduct a trial that cannot answer the research question that they
intended to ask.
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Signicant Limitations (E)
Step E asks investigators to explicate the limitations of the optimal comparator.
Unlike insurmountable barriers, limitations do not prevent an optimal comparator
from being implemented, but they often do affect the validity of the conclusions that
can be drawn from a trial. If, for example, a trial is designed to determine if the addi-
tion of a behavioral intervention to usual care improves the outcomes, the random-
ized arms would be usual care alone versus usual care plus the behavioral
intervention. The groups will differ with respect to the amount of contact. This does
not create a barrier to implementing the comparator, but it will make it difcult to
draw any rm conclusions about the extent to which contact time per se explains the
effects of the intervention.
Finalize Choice (F)
In Step F, the choice of the comparator is nalized. This choice places optimality
ahead of barriers and limitations when decisions about comparators are being made.
Many disagreements about comparators result from giving greater weight to barri-
ers and limitations than to the optimality of a comparator for the purpose of the trial.
One of the most important contributions of the Pragmatic Model is that it separates
and sequences the establishment of the primary purpose of the trial, the identica-
tion of the comparator that best ts this purpose, the recognition of barriers to the
use of the optimal comparator, and an explication of its limitations.
The Pragmatic Model will probably not answer every controversial question to the
satisfaction of everyone who has a strong opinion about how behavioral trials should
or should not be designed. However, it will help to resolve some of the most difcult
questions. It will also provide clarity to the research community about the selection
of comparators for behavioral trials. It can be cited by grant applicants and authors
of papers reporting on behavioral trials. It is hoped that this enhanced clarity will
have a signicant positive inuence on the selection and design of comparators in
the years ahead and, more generally, on the entire culture of behavioral clinical
trial design.
Purpose-Guided Comparator Choices
The principle that a comparator should t the purpose of the trial is a central premise
of the Purpose-Guided Trial Design (PGTD) Framework [12]. The main purpose of
a trial should be obvious to an investigator, but it often becomes confused. Such
confusion is exacerbated by the fact that comparator choices can change the primary
purpose of the trial. The PGTD Comparator Grid aims to help investigators clarify
the main purpose of the trial and then to align this purpose with the appropriate
comparator (Table 6.2). The use of the grid illuminates the intimate relationship
between comparator choice and the primary purpose of the trial.
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