Estimating current adoption rates
Although awareness of adaptive trial design use has grown and qualitative reports from biopharmaceutical companies indicate
that adoption is increasing, little quantitative data exists to characterize industry-wide adoption of this study design approach.
Recently, two independent studies have been conducted to establish and corroborate baseline measures of adoption.
The two independent studies chose a definition of adaptive trial design that is consistent with the current FDA regulatory
guidance. Specifically, adaptive trial designs are pre-planned adaptations, generated through the use of trial simulations and scenario planning,
of one or more specified clinical trial design elements that are modified and adjusted while the trial is underway, based
on an analysis of blinded and unblended interim data.
The FDA cites numerous adaptations that can be planned and prospectively written into the protocol. Examples include pre-planned
changes in study eligibility criteria (either for subsequent study enrollment or for a subset selection of an analytic population);
randomization procedure; treatment regimens of different study groups (e.g., dose level, schedule, duration); sample sizes
of the study (including early termination); concomitant treatments used; planned schedules of patient evaluations for data
collection (e.g., number of intermediate time points, timing of last patient observation and duration of patient study participation);
and analytic methods employed to evaluate protocol endpoints (e.g., covariates of final analysis, statistical methodology,
or Type I error control).
In October 2011, the Drug Information Association's (DIA) Adaptive Design Scientific Working Group (ADSWG) conducted an online
survey among 11 pharmaceutical and biotechnology companies and six contract research organizations (CROs). Participating companies
reported that 475 adaptive design trials had been conducted between January 2008 and September 2011, suggesting a 22% adoption
rate. Two-thirds (65%) of the total adaptive clinical trials analyzed used group sequential or blinded sample size re-estimation.
One-third (35%) employed other adaptive design approaches, including unblinded sample size re-estimation, added or dropped
treatment arms, and changes in randomization ratios.
In October 2012, the Tufts Center for the Study of Drug Development (Tufts CSDD) conducted in-depth interviews on the status
of adaptive design implementation among 12 major pharmaceutical companies. The study was funded by an unrestricted grant from
Aptiv Solutions. Tufts CSDD probed current adoption rates and their impact on study budgets and durations. The results of
this study were consistent with that conducted by the ADSWG. Overall, simple adaptive designs are being used on approximately
one out of five (20%) late-stage Phase III clinical trials. Early terminations due to efficacy futility were the most common
simple adaptive design used. Sample size re-estimation was also a commonly used adaptive design approach. In-depth interviews
with sponsor companies indicated low usage rates (i.e., 10% of clinical trials) of adaptive dose finding and treatment group
adaptations (e.g., dropping unsafe or ineffective doses) and extremely low usage of seamless Phase II/III studies.
While the two independent assessments indicate that between 20-22% of all active clinical trials include an adaptive trial
design approach, analyses of public and commercial databases of trial activity present a very different picture.
Two separate assessments of the Department of Health and Human Services' ClinicalTrials.Gov (CT.Gov) registry of FDA-regulated
clinical trials found very small numbers of adaptive trial designs listed there. Searching the term "adaptive design," the
ADSWG found only 62 adaptive trial design studies listed—among the 103,213 active trials listed in CT.Gov since 2008—a 0.06%
Tufts CSDD conducted a search of a broader set of adaptive trial design keywords among the 103,213 active CT.Gov trials listed
since 2008. Examples of keywords searched include adaptive design, Bayesian design, sample size re-estimation, and group sequential.
Tufts CSDD found 119 total trials, suggesting a 0.1% adoption rate. Tufts CSDD also manually searched 37,111 active 2012 clinical
trial listings in CT.Gov and found a total of 35 adaptive trial designs listed—a 0.09% adoption rate.
Tufts CSDD also conducted searches of adaptive design keywords using two commercially available subscription-based clinical
trial databases—Informa Health's Citeline and Thomson Reuters' Cortellis services. An assessment of the former database yielded
a 0.2% adoption rate (317 adaptive clinical trials out of 136,000 trials listed). Tufts CSDD found 134 adaptive clinical trial
designs out of a total of 146,678 trials listed in the latter database, suggesting a 0.09% adoption rate.
Given these contrasting survey results, the establishment of a robust method of monitoring the adoption of adaptive design
trials use and the specific types of adaptations utilized would be invaluable in improving senior management decision-making
on study design optimization practices and their impact. However, the extremely low adaptive trial adoption rates found in
CT.Gov and in commercially available databases are not plausible or credible given qualitative and quantitative assessments
of current adoption levels. These call into question the quality and integrity of the data on study design practices captured
As an immediate next step, Tufts CSDD and the ADSWG plan to meet with CT.Gov and EudraCT system administrators and with commercial
database developers (e.g., Informa Health, Citeline; Thomson Reuters, Cortellis; and Springer Science and Business Media,
Adis) to broadly discuss this problem; to establish consensus-based definitions of adaptive trial designs; and to develop
a formal process to capture more detailed, standardized data on various adaptive design approaches that can better inform
There is a critical need to improve the characterization of adaptive clinical trial designs in these public and commercial
databases. Doing so would assist regulators in anticipating changes in adaptive design practices and in assessing the impact
of regulatory reform on study design. Improvements in tracking adaptive design use will also benefit drug development sponsors
by providing better benchmarks on design practices and stimulating study design enhancements that may ultimately drive higher
levels of quality and improvements in drug development success rates.
Ken Getz is Director of Sponsored Research at the Tufts Center for the Study of Drug Development. He can be reached at firstname.lastname@example.org
. Phil Birch is Director at Aptiv Solutions. He can be reached at email@example.com
. Stella Stergiopoulos is Senior Project Manager at Tufts CSDD. She can be reached at firstname.lastname@example.org