Adaptive Dose Response Studies
Brenda Gaydos, Eli Lilly; Michael Krams, Wyeth; Inna Perevozskaya, Merck, Frank
Bretz, Novartis; Qing Liu, Johnson&Johnson, Paul Gallo, Novartis; Don Berry, MD
Anderson Cancer Center; Christy Chuang-Stein, Pfizer; José Pinheiro, Novartis; Alun
Bedding, Eli Lilly
Abstract. Insufficient exploration of the dose response is a shortcoming of clinical drug
development, and failure to characterize dosing early is often cited as a key contributor to
the high late-stage attrition rate currently faced by the industry. Adaptive methods, for
example, make it feasible to design a proof-of-concept study as an adaptive dose response
trial. Efficient learning about the dose response earlier in development will ultimately
reduce overall costs and provide better information on dose in the filing package. This
paper presents the PhRMA working group’s main recommendations regarding adaptive
dose response studies. As background, traditional fixed and adaptive dose response
designs are briefly reviewed. Information on developing a Bayesian adaptive dose design
and some monitoring and processing issues are also discussed.
1.
Introduction
Insufficient exploration of the dose response is often a key shortcoming of clinical drug
development. Initial proof-of-concept (PoC) studies often rely on testing just one dose
level (e.g. the maximum tolerated dose) without much information on which to base the
decision, assuming “more is better” and hoping the “right” dose was selected. Adaptive
designs (defined in [1]) offer efficient ways to learn about the dose response and guide
decision making on which dose(s) to select for further development, or whether to
discontinue a program. It is both feasible and advantageous to design a PoC study as an
adaptive dose response trial. The continuation of a dose response trial into a confirmatory
stage in a seamless design is a further opportunity to increase information on the right
dose(s) earlier in development and reduce the total clinical development timeline; see [2].
We envisage that efficient learning about the dose response earlier in development will
reduce overall costs and provide better information on dose in the filing package. Our
goals for the development of adaptive dose response designs are to facilitate:

more feasible approaches for gaining information on the dose response profile
earlier in development;

increasing the probability of technical success by taking the correct choice of
dose(s) forward into phase III, and thereby, reducing the late-stage attrition rate;

reducing the clinical development timeline by stopping at the earliest possible
timepoint either for futility or efficacy.
This paper primarily focuses on phase Ib and II study designs, using endpoints that
support filing or are predictive of the filing endpoint. For example, a biomarker might be
used as the primary endpoint in a small PoC study, or to adapt a trial where the primary
endpoint is not immediately observable, or be used more indirectly to improve a
predictive model which is driving dose selection. We do not discuss here exposureresponse modelling; however, we want to acknowledge its importance and note that
many of the adaptive design principles discussed directly apply. Exposure-response
models are crucial to understanding activity at the patient level. Knowledge of exposureresponse increases our understanding of dose response, and these models are useful in
developing adaptive dose response designs.
Section 2 presents our key recommendations. In Section 3 we briefly review traditional
fixed dose response designs. In Sections 4 and 5 we review adaptive dose response
designs spanning phases I-III, and provide information on developing Bayesian adaptive
dose designs in Section 6. We discuss monitoring issues and process in Section 7, and
introduce a relatively recent class of dose response designs (Rolling Dose) in Section 8.
2.
Recommendations
In general, we recommend the following:

Consider adaptive dose response designs in exploratory development. Both
FDA and EMEA are supportive and encourage the deployment of appropriately
planned adaptive designs in this segment of the critical path. Whether or not an
adaptive design is ultimately deployed should be the result of a critical feasibility
debate (see [3]), but if adaptive designs are not considered as an option, the debate
would not even occur.

Consider adaptive dose response designs to establish proof-of-concept. An
estimate of the dose response profile is more informative than an estimate of the
response at a single dose. The former is not frequently done, however. One reason
for this is the perception that the dose response cannot be estimated sufficiently
well with the limited budgets often available for very early go/no-go PoC studies.
Our objective is to show that adaptive designs could be viewed as an enabler
merging “PoC” and “dose response” into a single research question.

Whenever possible use an approach that incorporates a model for the dose
response. The intention is to make the best use of information that accrues in the
trial. Potential applications include longitudinal models where an early readout of
a biomarker predictive of final outcome is used to assist in decision making. This
may lead to faster learning, and allow earlier futility decisions based on the
2
biomarker readout. However, it is critical to assess the model assumptions before
deployment.

Consider seamless approaches to improve the efficiency of learning. Logistic
advantages include reduction of downtimes, and synergistic effects in setting up
study sites and other infrastructure. Integrating traditional phases into a single
experiment may enrich the information value derived from such studies. One
example of benefit could be an ongoing longer-term exploration of the
relationship between biomarkers and clinical endpoints.

Define the dose assignment mechanism prospectively and fully evaluate its
operational characteristics through simulation prior to initiating the study.
We strongly recommend building up experiences with adaptive designs that fully
characterize the decision rule in advance. This will help to ensure the validity of
study conclusions as well as facilitate ethical review and implementation. Health
authorities are rightly concerned that the concept of adaptive designs could be
misused for ad-hoc changes to remedy poor planning. A detailed layout of
possible decisions a priori and their potential impact on study conduct and
conclusions will help alleviate this concern.

Stop the trial at the earliest time point when there is enough information to
make the decision. Typically, a maximum sample size will need to be identified.
The design could include planning for the maximum sample and stopping early,
or planning to extend the trial until sufficient certainty (precision) has been
achieved up to some maximum sample. The later case allows for new information
to be considered in setting the maximum. In either case, we recommend routinely
incorporating futility analyses into early dose response studies due to the low
probability of success for novel candidates.

A committee must monitor the study on an ongoing basis to verify that the
performance of the design is as expected. The objectives of monitoring include
ensuring that errors in programming are not occurring, and that no adverse
responses are occurring that may affect the dose assignment but were not
accounted for in the adaptive algorithm. For early blinded studies, this could be a
sponsor-internal committee, as long as its members are adequately firewalled
from the project team and other parties involved in the conduct of the study [3,4].

Engage the committee early in scenario simulations prior to protocol
approval. The responsibilities of the committee would include overseeing
performance of the treatment allocation algorithm. This requires insight into both
the clinical aspects and the statistical methodology and operational characteristics
of the algorithm.
3

3.
Leverage the information from disease state and exposure-response models
to design studies. These models should be instrumental in developing the
adaptive mechanism and in assessing the performance of the algorithm under a
variety of scenarios of truth, especially for first-in-patient dose response studies.
For Bayesian applications, this information can also be incorporated into the
definition of the prior distributions for the dose-response model parameters of
interest.
Traditional methods to explore the dose response
Fixed-dose parallel-group designs are typically driven by the desire to understand the
average population response at a dose and the shape of the population dose response
curve for both efficacy and safety. Population average dose response information is
useful in determining the starting dose, and for adjusting dose for a population. In
addition, the shape of the population average dose response curve can help identify the
smallest dose with a discernible beneficial effect, or a maximum dose beyond which no
further benefits are seen.
These considerations have led to study designs that treat doses equally. Such designs
attempt to estimate the mean response at different doses with nearly identical precision.
There are two possible downsides to this approach. One is the potential to allocate a fair
number of patients to several non-informative doses, resulting in sub-optimal efficiency
in learning about the dose response. The other is that sample size considerations often
limit the number of doses feasible to explore. Therefore, if little is known about the true
dose response a priori, there can be a high likelihood that the study will not be
sufficiently informative.
Historically, four study design types have been used to explore dose response with some
regularity. They are the parallel group design, cross-over design, forced titration design,
and optional titration design. The latter three are primarily aimed at learning about
individual dose-response. Despite the differences in the primary objectives among these
four designs, they can be useful in describing the dose response relationship if properly
planned, executed and analyzed. These designs are covered in ICH E4 [5]. Other
recommended references include [6-8].
4.
Adaptive dose response methods for early exploratory
studies
This section provides a brief overview of traditional phase I designs with respect to
estimation of the Maximum Tolerated Dose (MTD), and discusses novel adaptive design
approaches aimed at improving the relatively poor performance of traditional designs (for
more detail see [9,10]). While the majority of these methods originated from oncology
4
and were designed to target the MTD only, there is methodological overlap with dose
response methods discussed later. The applicability of these methods can be generalized
from MTD determination to learning about the dose response profile for any defined
response (e.g. tolerability, safety or efficacy measure), and some of these approaches are
also discussed in this section. That being said, more work generally needs to be done
outside the area of cancer research since most of the literature on such methods is
restricted to the oncology context.
A monotonic relationship between dose and toxic responses is typically assumed in phase
I trials. There are two major philosophies regarding MTD definition:
1. The dose which, if exceeded, would put patients at unacceptable risk of toxicity.
Such a definition is vague from the statistician’s point of view since
“unacceptable risk” may not be defined quantitatively.
2. Specifying unacceptable risk of toxicity as a probability; therefore MTD is an
unknown parameter to be estimated corresponding to the specified probability.
These two definitions lead to two different approaches for designing phase I clinical
trials:
1. Conventional up-and-down designs (such as 3+3 design for cancer)
2. Model-based designs (MTD is a quantile to be estimated), e.g.:

Random Walk Rule

Bayesian methods (continual reassessment method, escalation with
overdose control, decision-theoretic approaches, Bayesian D-optimal
design)
Conventional 3+3 designs employ an ad-hoc approach to efficiently screen dose levels
and identify the MTD; no estimation in a traditional sense is involved. Patients are
treated in groups of three starting with the initial dose. “Toxicity” is defined as a binary
event. The algorithm iterates moving the dose up or down depending on the number of
toxicities observed, and MTD is defined as the highest dose studied with less than, say,
1/3 toxicities. These designs rely on a response rate within a range of 20%-30% to be
applicable and are not well suited for efficacy studies; these designs often provide poor
estimates of MTD, i.e. the probability of stopping at an incorrect dose level is higher than
generally perceived [11].
Random-Walk-Rule Designs (RWR) (“biased coin designs”) are non-parametric modelbased approaches to MTD estimation, i.e. the MTD is treated as a quantile of a dose
5
response distribution, but there is no underlying parametric family. RWR designs are a
generalization of conventional up-and–down methods, but, unlike those methods, they
provide a unified approach targeting any quantile of interest. As in conventional designs,
patients are treated sequentially and dose escalation occurs when no toxicities are
observed. However, the dose escalation rule is different: instead of applying a
deterministic rule, a ‘biased coin’ is flipped after observing each response; the algorithm
escalates to the next dose with probability p, where p depends on the targeted level of
response. RWR designs have advantages of being non-parametric, having a workable
finite distribution theory, and being simple and intuitive to implement. They generate a
cluster of doses around the quantile of interest and also posses some optimality properties
[12].
The Continual-Reassessment Method (CRM) originated as a Bayesian method for phase
I cancer trials of cytotoxic agents (Section 6 provides more details of Bayesian methods).
For a predefined set of doses and a binary response, CRM estimates MTD as the dose
level that yields a particular target proportion of responses (e.g., TD20). It assumes a
particular model (such as the logistic function) and the assignment of doses converges to
the MTD. The method operates by updating the posterior distribution of the model
parameters after observing each response. The dose for the next patient is chosen as the
one with (Bayesian current) probability of toxicity closest to the target response level. A
potential downside to the original CRM method [13] is that it can escalate doses too
quickly. Several modified CRM approaches which mitigate risk of toxicity exposure
above acceptable limits have been developed and implemented [13-17]. An example of
non-oncology CRM application can be found in [17].
Other Bayesian Designs
(1) Escalation With Overdose Control (see [18]). This is similar to CRM, but utilizes
a more flexible family of underlying dose response models and controls the
predicted probability of next assignment exceeding MTD. The latter is
particularly important in oncology trials due to the severity of side effects.
(2) Designs based on Bayesian Decision Theory. This is a wide class of methods
applying Bayesian Decision Theory tools to achieve various goals, such as
shortening trials, reducing sample size, maximizing information, and reducing
cost. These designs use gain functions (based on the desired goal) that are
sequentially updated after each patient’s (cohort’s) response. The next dose
assignment is determined by maximising the gain function. Whitehead and
Brunier [19] describe a design minimizing posterior variance of the MTD
estimator. This methodology is extended in [9,20] to looking at pharmacokinetic
data with two gain functions: the first chooses the highest dose possible subject to
a safety constraint, and the second uses D-optimality in optimizing the parameters
6
of the model. The methodology is extended further to evaluate efficacy and
toxicity dose response relationships in [21,22]. Section 6 provides an example of
application in a phase II trial.
(3) Bayesian D-optimal Designs (see [23]). Similar to decision-theoretic approaches
[19], these designs are concerned with the efficiency of estimation. Originally
developed for a two parameter logistic family for the underlying dose response
model, they can be used for a variety of models (i.e. wide class of parametric
families). These designs introduce a formal optimality criterion (D-optimality)
minimizing the determinant of the variance-covariance matrix of the model
parameter estimates. After a small internal “pilot study” designed to obtain an
initial idea about the distribution of model parameters, a sequential procedure
begins to allocate incoming patients to doses yielding maximum information
about the dose-response experiment (measured as the determinant of the inverse
of the variance-covariance matrix). The optimal allocation changes with each
subsequent posterior distribution update (after each patient or cohort). Additional
constraints can prevent assigning patients to highly toxic doses. These designs
target the overall dose response curve rather than the MTD only; therefore, any
level of response can be estimated.
Simultaneous assessment of efficacy and toxicity: Penalized D-optimal Designs (see
[24]). These non-Bayesian designs accomplish “learning” from the information
accumulated during the trial by updating the model parameters using the likelihood
function after each patient’s response (instead of updating a posterior distribution).
Similar to the Bayesian D-optimal design, the D-optimality criterion is applied at each
step of the sequential trial to maximize the expected increment of information about
efficacy and toxicity dose response. The optimization is done subject to a constraint
which can reflect ethical concerns, cost, sample size, etc. This flexibility in the constraint
and the underlying bivariate model make this approach particularly useful in early phase
trials, as the scope extends beyond MTD estimation, and allows a number of questions
involving efficacy and safety dose response relationships to be addressed simultaneously.
5.
Adaptive frequentist approaches for late stage exploratory
development
This section focuses on designs more typically applicable to phase II studies. Several
methods are available for adaptive dose selection in multistage clinical trial designs
which strongly control the overall type I error rate of incorrectly rejecting any null
hypothesis of no dose level effect. In the context of adaptive dose response trials,
multiplicity concerns typically arise due to (i) comparison of several doses with a control
and (ii) multiple interim looks at the data for decision making. Performing each
hypothesis test at the nominal level α intended for the whole trial would inflate the
7
overall type I error; the significance levels of the individual tests therefore have to be
adjusted appropriately. For more details on multiple testing see [25,26].
Classical Group Sequential Designs are a type of adaptive design where the planned
sample size or amount of information may be reduced based on outcome data if the trial
or an arm of the trial is stopped early. In classical group sequential trials, test statistics
produced at interim analyses are compared to upper and/or lower stopping limits that
have been determined to jointly ensure the desired operating characteristics, and in
particular to control the overall type I error rate. Different methods have been proposed
to determine stopping boundaries [27-30]. Further details on group sequential methods
are given in [31].
Stallard and Todd [32] extended classical group sequential procedures to multi-armed
clinical trials in which patients are initially randomized between several dose groups and
a control. During the course of the trial, the best experimental treatment is identified
based on the maximum standardized test statistic at interim.
Adaptive Designs have been developed that offer far more flexibility for adaptation
within the multistage framework. These methods offer a high level of flexibility for
decision making during the trial, such as increasing sample size based on the observed
effect, modifying the patient population, or adapting doses; see [33,34] for reviews. They
require little pre-specification of decision rules before the beginning of a trial, and
therefore, the total information available at each interim time point can be used in
designing or adjusting the next stage. However, some of these approaches may lose
efficiency relative to classical group sequential designs [35,36].
The various approaches differ in how evidence is combined across stages, and for all
approaches this has to be pre-specified. When early stopping is allowed, the decision to
continue the trial to the next stage or to stop for futility is based on unblinded data
collected up to the interim time point. If the trial continues to the final stage, the final
analysis combines the information across all stages [37].
Lehmacher and Wassmer [38] established a connection between adaptive and classical
group sequential designs. They suggested combining evidence from different stages
using weighted sums of the inverse normal distribution function calculated at the
observed p-values. A general framework for adaptively changing hypotheses at interim
was presented in [39]. This method is based on the closure principle [40] for controlling
the overall type I error rate. The closure principle considers all intersection hypotheses
constructed from the initial hypotheses set of all pairwise comparisons with a control; a
dose is declared significant if all intersection hypotheses related to the dose are also
rejected.
8
Further Methods. A different approach is to use a standard single-stage multiple testing
procedures to account for the multiplicity arising from multiple comparisons versus the
control. The conduct of the study remains the same as in the previous methods, with the
understanding that doses may be dropped for statistical or non-statistical reasons during
the course of the trial. Irrespective of how many treatment arms continue to the end, the
final analysis uses standard multiplicity adjustments such as Dunnett [41] or Bonferroni.
As shown in [42], such approaches are often equally as powerful as adaptive design test
procedures, while being simpler to perform and allowing the derivation of simultaneous
confidence intervals.
In late-stage clinical trials for which the clinical endpoint of interest is only slowly
available relative to the patient accrual rate, adaptation based on the clinical endpoint
becomes impractical, if not impossible. To circumvent this problem, Liu and Pledger [43]
proposed a two-stage design combining phase II and III trials into a single study in which
an early efficacy endpoint during the phase II component is used in the interim analysis
as a surrogate for the clinical endpoint of interest. The methodology allows dropping of
low doses for lack of efficacy, or high doses for safety or tolerability concerns. Doses not
dropped at interim continue into the phase III component of the trial. The final analysis
uses combined pairwise test statistics of the phase II stage, and adaptive trend test
statistics of the phase III stage, where scales for trend are derived from a sigmoid Emax
modeling of the phase II data. Incorporation of the dose-response information in the
adaptive trend statistic results in a procedure that is both powerful and robust. Another
example of combining phase II and III using a surrogate is shown in [44].
6.
Developing a Bayesian adaptive dose design
The prime advantage of the Bayesian approach [45-47] is its focus on updating
information as it accrues [48]. This makes it ideal for sequential experimentation,
including clinical trials. The Bayesian approach also enables calculation of predictive
probabilities of future results for any particular design, which allows comparison of
designs on the basis of probabilities of their consequences. It also allows for addressing
the increment in information about a dose response curve depending on the dose assigned
to the next patient. Although control of the type I error rate is not a relevant property of a
Bayesian design, simulations can be used to design a Bayesian adaptive trial that also
maintains frequentist properties. A potential downside to the Bayesian approach is the
computational complexity coupled with the absence of commercial software packages to
assist with study design and analysis.
A critical step in the Bayesian approach is positing a model for the data to be observed in
the trial. The model can be flexible and have many parameters. It need not be tractable
mathematically, but must have parameters whose distributions can be updated from the
accruing trial data. An additional ingredient in the Bayesian approach is a prior
9
distribution for unknown parameters. This distribution can be “non-informative” or can
include historical information that may be available about the natural history of the
disease or the experimental agent or related agents. Efficiency is gained by incorporation
objective information in the prior, appropriately. Simulations should be performed to
understand the potential impact of the prior on the operating characteristics of the design
and the potential impact on the resulting posterior distribution.
Bayesian approaches are standard in phase I cancer trials of cytotoxic agents (see Section
4) and are increasingly used in phase II dose response trials. Again the advantage lies in
allowing updating of information about unknown aspects of the dose response curve. For
example, Berry et al. [49] describe the design of a trial to investigate a neuroprotective
agent for stroke and show how to proceed sequentially, analyzing the data in the trial as it
accumulates. A flexible model was used (normal dynamic linear model, [50]) to estimate
the dose response curve. This model does not restrict the shape of the curve, and allows
for non-monotonicity if indicated by the data. In that example there are two trial stages,
first dose-ranging and then confirmatory (if the latter is warranted). The dose-ranging
stage continues until a decision is made that the drug is not sufficiently effective to
pursue future development, or that the optimal dose for the confirmatory stage has been
sufficiently well identified. Switches to the confirmatory stage can be made seamlessly
[2]. Each entering patient is assigned the dose (one of 16 including placebo) that
maximizes information about the dose response relationship, given the results observed
so far. In particular, patients are not assigned doses in regions where evidence suggests
that the dose response curve is flat. Note that the principle applies to whatever utility we
choose to optimize our learning against: the dose response: the ED95 or ED50, a gain
function, etc.
In the dose-ranging stage, neither the number of patients assigned to any particular dose
nor the total number of patients assigned in this stage are fixed in advance. The doseranging sample size can be large when the drug has marginal benefit, when the dose
response curve is gently sloping, or when the standard deviation of the responses is
moderately large. It tends to be small if the drug has substantial benefit or has no benefit,
if the dose response curve rises over a narrow range of doses, or if the standard deviation
of the responses is small. (In addition, and somewhat non-intuitively, the dose-ranging
stage is small if the standard deviation of responses turns out to be very large. The reason
is that a sufficiently large standard deviation implies that a very large sample size is
required to show a beneficial effect. The required sample size may be so large that it
makes it impossible to study the drug and so the trial stops in the dose-ranging phase
before substantial resources go down the drain.)
The stroke trial considered in [49,51] is important in showing how long-term endpoints
can be handled in the Bayesian approach. Again, the answer is in modeling. The
ultimate endpoint is the improvement in the stroke scale from baseline to 13 weeks. If
10
the accrual rate is sufficiently large then the benefit of adaptive assignment is limited by
delays in obtaining endpoint information. However, each patient’s stroke scale was
assessed weekly between baseline and week 13. Within-patient measurements are
correlated, with correlations greater if they are closer together in time. A longitudinal
model enabled Bayesian predictions of the ultimate patient-specific endpoint based on the
current patient-specific information. The circumstances of this trial are typical of many
types of trials. The adaptive nature of the stroke trial would be less efficient if early
endpoints were not exploited. Many diseases and conditions are characterized by the
availability of such early endpoints: information about how a patient is doing (local
control of the disease, biomarkers, etc.) before reaching the primary endpoint. Bayesian
modeling is an ideal way to incorporate such information.
In comparison to a standard fixed design, a Bayesian adaptive dose response design is
more effective in identifying “the right dose.” Additionally, it usually identifies this dose
with a smaller sample size than when using fixed-dose assignments. Another advantage
is that many more doses can be considered - even though some doses will be little used in
the trial, and some might never be used. Also, the Bayesian adaptive approach is
efficient at identifying drugs that have little or no benefit, as in the case of the stroke trial
[51].
7.
Monitoring issues and processes in adaptive dose
response trials
Adaptive dose response trials can of course occur throughout most stages of clinical
development, from phase I exploratory trials to confirmatory studies (and may in fact
combine stages or minimize traditional interpretations of development stages). All
adaptive trials which utilize unblinded data for within-trial decision making, or in which
specific changes made can convey to observers some information about interim results,
raise issues and potential concerns about how the adaptive aspects of the trials should
best be implemented in the interest of enhancing the credibility of trial conclusions.
As with most types of interim data monitoring, it is beneficial with regard to minimizing
potential for bias in the trial and enhancing the credibility of its conclusions for trial
participants and sponsor representatives not to have access to unblinded interim results,
beyond the extent necessary to meet the needs of the trial and the clinical program [52].
Thus, it is preferable to have a separate body without other direct trial responsibilities
review the interim results and make recommendations for the adaptations. Operational
models for monitoring ongoing trials are discussed in [4].
The nature of recommended adaptations may convey some information to trial
participants.
For example, if the methodology being used involves adaptive
randomization, as discussed earlier in this paper, it may be unavoidable for the change in
11
the randomization scheme to provide some limited comparative information regarding
how some dose groups are performing relative to others. Discontinuation of dose groups
in an adaptive dose response trial, or the addition of new doses, may convey some
information about the shape of the dose response curve. However the knowledge
conveyed about the magnitude of treatment effects and any resulting potential for bias
would be expected to be quite minimal as long as the specific numerical information
remains confidential, and should thus not be viewed as compromising the trial. If
possible, some detail on the statistical methodology and thresholds for adaptations might
be withheld from the trial protocol and documented elsewhere, to limit the extent of
information which might be inferred by observers, as long as this information were not
considered necessary for review boards, investigators, or patients to have knowledge of in
order to be sufficiently informed for their participation in the trial.
For trials which are planned to be confirmatory, and even for non-confirmatory trials
which could potentially play a supportive role in a regulatory submission, it is important
to try to adhere closely to current monitoring and confidentiality conventions and to limit
knowledge of interim results, in order to enhance the credibility and interpretability of the
trial results.
8.
Rolling Dose Studies
The term Rolling Dose Studies (RDS) refers to a broad class of designs and methods that
allow flexible, dynamic allocation of patients to dose levels as the trial progresses. RDS
are not a distinct set of methods, and many of the approaches discussed in this paper
could be considered to be within this class. In RDS, dose level arms can be started or
discontinued during the trial. Unlike classical group sequential methods and multi-stage
adaptive designs aimed at preserving the overall type I error rate, RDS are intended to
maximize the learning of the underlying dose response relationship and/or the precision
of estimated doses to achieve certain target effects (efficacy and safety). To achieve
these goals, RDS typically rely more on modelling techniques than on hypothesis testing
approaches.
The Bayesian adaptive dose response stroke study discussed in Section 6 is an example of
a RDS; however, not all RDS are Bayesian. Accruing unblinded data are utilized in
RDS, often in combination with dose response models, to determine the current best
allocation of patients to dose levels according to some optimality criterion (e.g., Doptimality, or minimum variance of target dose estimator) related to dose response or
target dose estimation. Alternative RDS approaches differ in the criteria used to define
the optimal allocation of patients to dose, the frequency of interim looks for recalculating
allocation fractions, and stopping rules (success in estimating dose response and/or target
doses, or futility).
12
PhRMA has constituted a working group on RDS as part of its Pharmaceutical
Innovation Steering Committee (PISC) initiative. This group is evaluating and
developing different RDS methods, and comparing them to more traditional non-adaptive
dose finding approaches (e.g., ANOVA with multiplicity correction) via a comprehensive
simulation study. The conclusions from the simulation and recommendations from the
group on the practical use of RDS will be included in a white paper to be submitted for
publication.
9.
Conclusion
We recommend routinely assessing the appropriateness of adaptive designs, including
adaptive dose response designs, when developing plans for clinical programs. The
development of commercial software packages could greatly facilitate this. Adaptive
designs present an opportunity to efficiently gain more information about the dose
response at any point in development; however, we recommend gaining this information
early for maximum benefit. A good example of a PoC study in pain employing a
Bayesian adaptive dose design early is presented in [53]. It seems reasonable to expect
that obtaining more information on the dose response earlier in development could enable
confidence in a more streamlined phase III clinical plan, reducing timelines and costs, yet
increasing information at time of filing.
We point out, however, that adaptive designs are not necessarily always better than fixed
designs, and that there are many adaptive design possibilities to select from. Thorough
early planning is required to ensure that benefits are achieved. The added scientific and
operational complexity should be justified. Simulations are often needed, under realistic
scenarios of truth, to assess how the design will perform and to compare performance
across designs. The adaptive mechanism should be detailed enough to enable simulations
prior to finalizing the protocol. Dose selection should not be a black box. In addition to
streamlining implementation, clarity on the criteria of dose selection will facilitate ethical
review.
We cannot emphasize strongly enough the importance of planning. Adaptive dose
response designs have built-in flexibility to address areas of uncertainty about the dose
response. As with any trial design, protocol amendments may be required, but may be
less likely to be needed than in a fixed design where learning is not built in. Emphasis
needs to be made upfront on the questions to be addressed regarding the dose response,
and the adaptive trial needs to be designed accordingly. For example, it might be
desirable to address one or all of the following to different degrees: minimally effective
dose; ED95; a particular effect level beyond that of a comparator treatment. With a
Bayesian dose-adaptive approach, for example, the utility function can be designed so
that the adaptive algorithm will prospectively weight future treatment assignments to
optimize information around the key questions.
13
We recognize that there are limited examples of adaptive dose response applications
published in the literature, and more practical experience is needed. We thank Andy
Grieve for some additional references [54-57]. We welcome further exploration and
publications in this area.
References
1. Dragalin V. Adaptive designs: terminology and classification. Drug Inf J. 2006
(submitted)
2. Maca J, Bhattacharya S, Dragalin V, Gallo P, Krams M. Adaptive seamless phase II /
III designs – Background, operational aspects, and examples. Drug Inf J. 2006
(submitted).
3. Quinlan JA, Krams M. Implementing adaptive designs: logistical and operational
considerations. Drug Inf J. 2006 (submitted).
4. Gallo P. Confidentiality and trial integrity issues for adaptive designs. Drug Inf J.
2006 (submitted).
5. International Conference on Harmonisation Expert Working Group. Guideline for
industry: Dose response information to support drug registration. Federal Register
1994;59(216):55972-55976.
6. Ting N, ed. Dose Finding in Drug Development. Springer;2006.
7. Ruberg SL. Dose response studies. I. Some design considerations. J Biopharm Stat.
1995;5(1):1-14.
8. Ruberg SL. Dose response studies. II. Analysis and interpretation. J Biopharm Stat.
1995;5(1):15-42.
9. Whitehead J, Zhou Y, Patterson S, Webber D, Francis S. Easy-to-implement
Bayesian methods for dose-escalation studies in healthy volunteers. Biostatistics
2001;2:47-61.
10. Rosenberger WF, Haines LM. Competing designs for phase I clinical trials: a review.
Stat Med. 2002;21:2757-2770.
11. Reiner E, Paoletti X, O’Quigley J. Operating characteristics of the standard phase I
clinical trial design. Computational Statistics and Data Analysis 1999;30:303-315.
12. Durham SD, Flournoy N. Random walks for quantile estimation. In Gupta SS, Berger
JO, ed. Statistical Decision Theory and Related Topics. New York:
Springer;1994:467–476.
14
13. O’Quigley J, Pepe M, Fisher L. Continual reassessment method: a practical design for
phase 1 clinical trials in cancer. Biometrics 1990;46:33–48.
14. Faries D. Practical modifications of the continual reassessment method for phase I
cancer clinical trials. J Biopharm Stat. 1994;4:147-164.
15. Korn EL, Midthune D, Chen TT, Rubinstein LV, Christian MC, Simon RM. A
comparison of two phase I designs. Stat Med. 1994;13:1799-1806.
16. Goodman SN, Zahurak ML, Piantadosi S. Some practical improvements in the
continual reassessment method for phase I studies. Stat Med. 1995;14:1149-1161.
17. Dougherty TB, Porche VH, Thall PF. Maximum tolerated dose of Nalmefene in
patients receiving epidural fentanyl and dilute bupivacaine for postoperative
analgesia. Anesthesiology 2000;92(4):1010-1016.
18. Babb J, Rogatko A, Zacks S. Cancer phase I clinical trials: efficient dose escalation
with overdose control. Stat Med. 1998;17:1103-1120.
19. Whitehead J, Brunier H. Bayesian decision procedures for dose determining
experiments. Stat Med. 1995;14:885-893.
20. Patterson S, Jones B. Bioequivalence and Statistics in Clinical Pharmacology
London: Chapman & Hall;2005.
21. Whitehead J, Zhou Y, Stevens J, Blakey G. An evaluation of a Bayesian method of
dose escalation based on bivariate binary responses. J Biopharm Stat.
2004;14(4):969-983.
22. Braun T. The bivariate continual reassessment method: extending the CRM to phase I
trials of two competing outcomes. Contr Clin Trials 2002;23:240-256.
23. Haines LM, Perevozskaya I, Rosenberger WF. Bayesian optimal designs for phase I
clinical trials. Biometrics 2003;59:561-600.
24. Dragalin V, Fedorov V. Adaptive designs for dose-finding based on efficacy-toxicity
response. Journal of Statistical Planning and Inference 2005;136:1800-1823.
25. Hochberg Y, Tamhane AC. Multiple Comparison Procedures. Wiley: New
York;1987.
26. Hsu JC. Multiple Comparisons. London: Chapman and Hall;1996.
15
27. Lan KKG, DeMets DL. Discrete sequential boundaries for clinical trials. Biometrika
1983;70:659-663.
28. Pocock SJ. Group sequential methods in the design and analysis of clinical trials.
Biometrika 1977;64:191-199.
29. O'Brien PC, Fleming TR. A multiple testing procedure for clinical trials. Biometrics
1979;35:549-556.
30. Whitehead J. The Design and Analysis of Sequential Clinical Trials. Revised 2nd
Edition, Chichester: Wiley;1997.
31. Jennison C, Turnbull BW. Group Sequential Methods with Applications to Clinical
Trials. London: Chapman and Hall;2000.
32. Stallard N, Todd S. Sequential designs for phase III clinical trials incorporating
treatment selection. Stat Med. 2003;22:689-703.
33. Jennison C, Turnbull BW. Meta-analyses and adaptive group sequential designs in
the clinical development process. J Biopharm Stat. 2005;15:537-558.
34. Bauer P, Brannath W. The advantages and disadvantages of adaptive designs for
clinical trials. Drug Discovery Today 2004;9(8):351-357.
35. Tsiatis AA, Mehta C. On the inefficiency of the adaptive design for monitoring
clinical trials. Biometrika 2003;90:367-378.
36. Brannath W, Bauer P, Posch M. On the efficiency of adaptive designs for flexible
interim decisions in clinical trials. Journal of Statistical Planning and Inference
2006;136:1956-1961.
37. Bauer P, Köhne K. Evaluation of experiments with adaptive interim analyses.
Biometrics 1994;50:1029-1041.
38. Lehmacher W, Wassmer G. Adaptive sample size calculations in group sequential
trials. Biometrics 1999;55:1286-1290.
39. Hommel G. Adaptive modifications of hypotheses after an interim analysis.
Biometrical J. 2001;43:581-589.
40. Marcus R, Peritz E, Gabriel KB. On closed testing procedures with special reference
to ordered analysis of variance. Biometrika 1976;63:655-660.
16
41. Dunnett CW. A multiple comparison procedure for comparing several treatments
with a control. J Am Stat Assoc. 1955;50:1096-1121.
42. Bretz F, Schmidli H, König F, Racine A, Maurer W. Confirmatory seamless phase
II/III clinical trials with hypothesis selection at interim: General concepts.
Biometrical J. 2006 (in press).
43. Liu Q, Pledger WG. Phase 2 and 3 combination designs to accelerate drug
development. J Am Stat Assoc. 2005;100:493-502.
44. Todd S, Stallard N. A new clinical trial design combining phases II and III: sequential
designs with treatment selection and a change of endpoint. Drug Inf J. 2005;39:109118.
45. Spiegelhalter DJ, Abrams KR, Myles JP. Bayesian Approaches to Clinical Trials and
Health-Care Evaluation. Chichester: Wiley;2004.
46. Berry DA. Statistical Innovations in Cancer Research. In Holland J, Frei T ed. Cancer
Medicine e.7. Ch 29. London: BC Decker;2005.
47. Berry DA. Bayesian clinical trials. Nature Reviews Drug Discovery 2006;5:27-36.
48. Berry DA. Statistics: A Bayesian Perspective. Belmont, CA: Duxbury Press;1996.
49. Berry DA, Müller P, Grieve AP, Smith M, Parke T, Blazek R, Mitchard N, Krams M.
Adaptive Bayesian Designs for Dose-Ranging Drug Trials. In Gatsonis C, Carlin B,
Carriquiry A ed. Case Studies in Bayesian Statistics V 99-181. New York: SpringerVerlag;2001.
50. West M, Harrison J. Bayesian Forecasting and Dynamic Models, 2nd edition.
Springer Verlag;1997.
51. Krams M, Lees KR, Hacke W, Grieve AP, Orgogozo J-M, Ford GA. Acute stroke
therapy by inhibition of neutrophils (ASTIN): An adaptive dose response study of
UK-279,276 in acute ischemic stroke. Stroke 2003;34:2543-2548.
52. US Food and Drug Administration. Guidance for Clinical Trial Sponsors on the
Establishment and Operation of Clinical Trial Data Monitoring Committees.
Rockville MD: FDA;2006. (Available at
http://www.fda.gov/cber/gdlns/clintrialdmc.htm)
53. Smith MK, Jones I, Morris MF, Grieve AP, Tan K. Implementation of a Bayesian
adaptive design in proof of concept study. Pharmaceutical Statistics 2006;5:39-50.
17
54. Roon KI, Olesen J, Diener HC, Ellis P, Hettiarachchi J, Poole PH, Christianssen I,
Kleinermans D, Kok JG, Ferrari MD. No acute antimigraine efficacy of CP-122,288,
a highly potent inhibitor of neurogenic inflammation: Results of two randomized,
double-blind, placebo controlled clinical trials. Annals of Neurology 2000;47(2):238241.
55. Farge D, Marolleau JP, Zohar S, Marjanovic Z, Cabane J, Mounier N, Hachulla E,
Philippe P, Sibilia J, Rabian C, Chevret S, Gluckman E. Autologous bone marrow
transplantation in the treatment of refractory systemic sclerosis: early results from a
French multicentre phase I-II study. Br J Haematology 2002;119(3):726-739.
56. Camorcia M, Capogna G, Lyons G, Columb M. Epidural test dose with
levobupivacaine and ropivacaine: determination of ED50 motor block after spinal
administration. Br J Anaesthesia 2004;92(6):850-853.
57. Desfrere L, Zohar S, Morville P, Brunhes A, Chevret S, Pons G, Moriette G, Rey E,
Treluyer JM. Dose-finding study of ibuprofen in patent ductus arteriosus using the
continual reassessment method. Journal of Clinical Pharmacy & Therapeutics
2005;30(2):121-132.
18