An adaptive clinical trial, also called a "Bayesian clinical trial", is a dynamic clinical trial that evaluates a medical device or treatment by observing participant outcomes (and possibly other measures, such as side-effects) on a prescribed schedule, and, uniquely, modifying parameters of the trial protocol in accord with those observations. This is in contrast to traditional randomized clinical trials (RCTs) that are static in their protocol and do not modify any parameters until the trial is completed. The adaptation process generally continues throughout the trial, as prescribed in the trial protocol. Adaptions may include modifications to: dosage, sample size, drug undergoing trial, patient selection criteria and/or "cocktail" mix.^[1] In some cases, trials have become an ongoing process that regularly adds and drops therapies and patient groups as more information is gained.^[2] Importantly, the trial protocol is set before the trial begins which pre-specifies the adaptation schedule and processes.

Purpose[edit]

The aim of an adaptive trial is to more quickly identify drugs or devices that have a therapeutic effect, and to zero in on patient populations for whom the drug is appropriate.^[3] When conducted efficiently, adaptive trials have the potential to find new treatments while minimizing the number of patients exposed to the risks of clinical trials. Specifically, adaptive trials can efficiently discover new treatments by reducing the number of patients enrolled in treatment groups that show minimal efficacy or higher adverse-event rates. Adaptive trials can adjust almost any part of its design, based on pre-set rules and statistical design, such as sample size, adding new groups, dropping less effective groups and changing the probability of being randomized to a particular group, for example.

Types[edit]

Any trial design that can change its design, during active enrollment, could be considered an adaptive clinical trial. There are a number of different types, and real life trials may combine elements from these different trial types:^[4][5][6]

Trial Design Type	Adaptable Element	Description
Adaptive dose-finding	Treatment dose	Dose may be changed to find minimally toxic, and maximally effective dosing.
Adaptive hypothesis	Trial endpoints	According to pre-set protocols, these trials can adapt to investigate new hypothesis, and add new endpoints accordingly. An example would be switch from a superiority to a non-inferiority design.
Adaptive group sequential	Sample size, by a set interval at a time.	Sample sizes can be changed. These trials usually change the sample size by adding or removing set-blocks of patients such as adding 20 patients at a time, and then re-evaluating.
Adaptive randomisation	Randomization ratios	The chance of being randomize into one particular group, can change. Treatment groups are not added or dropped, but the chance of being randomized, for example, into the treatment group could increase after interim analysis.
Adaptive treatment-switching	Treatment	These trials, based on pre-set rules, can change individual patients from one group to another.
Biomarker adaptive	Multiple, on the basis of biomarker discoveries	These trials incorporated biomarkers into their decision making process. Examples including focusing on a sub-population that may be more biological receptive to a treatment, or choosing new treatments for a trial as more becomes known about the biology of the disease.
Population enrichment	Population enrolled	The population that the trial enrolls from may change based on, for example, improved epidemiological understanding of a disease.
Platform Trial	Multiple, on the basis that all different treatment groups share the same, single control group	Platform trials are defined by having a constant control group, against which variable treatment groups are compared.
Pick-the-winner/drop-the-loser	Treatment groups on the basis of superiority/inferiority rules	These trials actively adapt to stop investigating drugs when they show less efficacy on interim analysis, and therefor adapt to allocate enrolled patients to more effective groups.
Sample size re-estimation	Sample size	Sample sizes of either the whole trial or individual groups may change as more becomes known about effect sizes.
Seamless Phase I/II	Entry into phase II trials	These trials collect data on safety and dosing simultaneously.
Seamless Phase II/III	Entry into phase III trials	These trials collect data on dosing and efficacy simultaneously.

History[edit]

In 2004, a Strategic Path Initiative was introduced by the United States Food and Drug Administration (FDA) to modify the way drugs travel from lab to market. This initiative aimed at dealing with the high attrition levels observed in the clinical phase. It also attempted to offer flexibility to investigators to find the optimal clinical benefit without affecting the study's validity. Adaptive clinical trials initially came under this regime.^[2]

The FDA issued draft guidance on adaptive trial design in 2010.^[3] In 2012, the President's Council of Advisors on Science and Technology (PCAST) recommended that FDA "run pilot projects to explore adaptive approval mechanisms to generate evidence across the lifecycle of a drug from the pre-market through the post-market phase." While not specifically related to clinical trials, the council also recommended that FDA "make full use of accelerated approval for all drugs meeting the statutory standard of addressing an unmet need for a serious or life-threatening disease, and demonstrating an impact on a clinical endpoint other than survival or irreversible morbidity, or on a surrogate endpoint, likely to predict clinical benefit."^[7]

By 2019, the FDA updated their 2010 recommendations and issued "Adaptive Design Clinical Trials for Drugs and Biologics Guidance".^[8]

Statistical Analysis[edit]

The adjustable nature of adaptive trials inherently suggests the use of Bayesian statistical analysis. Bayesian statistics inherently address updating information such as that seen in adaptive trials that change from updated information derived from interim analysis. The problem of adaptive clinical trial design is more or less exactly the bandit problem as studied in the field of reinforcement learning.

Bayesian designs[edit]

According to FDA guidelines, an adaptive Bayesian clinical trial can involve:^[9]

• Interim looks to stop or to adjust patient accrual
• Interim looks to assess stopping the trial early either for success, futility or harm
• Reversing the hypothesis of non-inferiority to superiority or vice versa
• Dropping arms or doses or adjusting doses
• Modification of the randomization rate to increase the probability that a patient is allocated to the most appropriate treatment (or Arm in the Multi-Armed Bandit model)

The Bayesian framework Continuous Individualized Risk Index which is based on dynamic measurements from cancer patients can be effectively used for adaptive trial designs. Platform trials rely heavily on Bayesian designs.

Statistical Analysis[edit]

The problem of adaptive clinical trial design is more or less exactly the bandit problem as studied in the field of reinforcement learning.

Logistics[edit]

The logistics of managing traditional, fixed format clinical trials are quite complex. Adapting the design as results arrive adds to the complexity of design, monitoring, drug supply, data capture and randomization.^[2] However, according to PCAST "One approach is to focus studies on specific subsets of patients most likely to benefit, identified based on validated biomarkers. In some cases, using appropriate biomarkers can make it possible to dramatically decrease the sample size required to achieve statistical significance—for example, from 1500 to 50 patients."^[10]

Disease targets[edit]

Breast cancer[edit]

An adaptive trial design enabled two experimental breast cancer drugs to deliver promising results after just six months of testing, far shorter than usual. Researchers assessed the results while the trial was in process and found that cancer had been eradicated in more than half of one group of patients. The trial, known as I-Spy 2, tested 12 experimental drugs.^[3]

I-SPY 1[edit]

For its predecessor I-SPY 1, 10 cancer centers and the National Cancer Institute (NCI SPORE program and the NCI Cooperative groups) collaborated to identify response indicators that would best predict survival for women with high-risk breast cancer. During 2002–2006, the study monitored 237 patients undergoing neoadjuvant therapy before surgery. Iterative MRI and tissue samples monitored the biology of patients to chemotherapy given in a neoadjuvant setting, or presurgical setting. Evaluating chemotherapy's direct impact on tumor tissue took much less time than monitoring outcomes in thousands of patients over long time periods. The approach helped to standardize the imaging and tumor sampling processes, and led to miniaturized assays. Key findings included that tumor response was a good predictor of patient survival, and that tumor shrinkage during treatment was a good predictor of long-term outcome. Importantly, the vast majority of tumors identified as high risk by molecular signature. However, heterogeneity within this group of women and measuring response within tumor subtypes was more informative than viewing the group as a whole. Within genetic signatures, level of response to treatment appears to be a reasonable predictor of outcome. Additionally, its shared database has furthered the understanding of drug response and generated new targets and agents for subsequent testing.^[11]

I-SPY 2[edit]

I-SPY 2 is an adaptive clinical trial of multiple Phase 2 treatment regimens combined with standard chemotherapy. I-SPY 2 linked 19 academic cancer centers, two community centers, the FDA, the NCI, pharmaceutical and biotech companies, patient advocates and philanthropic partners. The trial is sponsored by the Biomarker Consortium of the Foundation for the NIH (FNIH), and is co-managed by the FNIH and QuantumLeap Healthcare Collaborative. I-SPY 2 was designed to explore the hypothesis that different combinations of cancer therapies have varying degrees of success for different patients. Conventional clinical trials that evaluate post-surgical tumor response require a separate trial with long intervals and large populations to test each combination. Instead, I-SPY 2 is organized as a continuous process. It efficiently evaluates multiple therapy regimes by relying on the predictors developed in I-SPY 1 that help quickly determine whether patients with a particular genetic signature will respond to a given treatment regime. The trial is adaptive in that the investigators learn as they go, and do not continue treatments that appear to be ineffective. All patients are categorized based on tissue and imaging markers collected early and iteratively (a patient's markers may change over time) throughout the trial, so that early insights can guide treatments for later patients. Treatments that show positive effects for a patient group can be ushered to confirmatory clinical trials, while those that do not can be rapidly sidelined. Importantly, confirmatory trials can serve as a pathway for FDA Accelerated Approval. I-SPY 2 can simultaneously evaluate candidates developed by multiple companies, escalating or eliminating drugs based on immediate results. Using a single standard arm for comparison for all candidates in the trial saves significant costs over individual Phase 3 trials. All data are shared across the industry.^[11] As of January 2016^[update] I-SPY 2 is comparing 11 new treatments against 'standard therapy', and is estimated to complete in Sept 2017.^[12] By mid 2016 several treatments had been selected for later stage trials.^[13]

Alzheimer's[edit]

Researchers plan to use an adaptive trial design to help speed development of Alzheimer's disease treatments, with a budget of 53 million euros. The first trial under the initiative was expected to begin in 2015 and to involve about a dozen companies.^[3]

Risks[edit]

Shorter trials may not reveal longer term risks, such as a cancer's return.^[3]

See also[edit]

References[edit]

Sources[edit]

• Kurtz, Esfahani, Scherer (July 2019). "Dynamic Risk Profiling Using Serial Tumor Biomarkers for Personalized Outcome Prediction". Archived from the original on 2019-08-11. Retrieved 2020-01-22.{{cite web}}: CS1 maint: multiple names: authors list (link)
• President's Council of Advisors on Science and Technology (September 2012). "Report To The President on Propelling Innovation in Drug Discovery, Development and Evaluation" (PDF). Executive Office of the President. Archived (PDF) from the original on 2017-01-21. Retrieved 2014-01-04.
• Brennan, Zachary (2013-06-05). "CROs Slowly Shifting to Adaptive Clinical Trial Designs". Outsourcing-pharma.com. Retrieved 2014-01-05.
• Spiegelhalter, David (April 2010). "Bayesian methods in clinical trials: Has there been any progress?" (PDF). Archived from the original (PDF) on 2014-01-06.
• Carlin, Bradley P. (March 25, 2009). "Bayesian Adaptive Methods for Clinical Trial Design and Analysis" (PDF).

External links[edit]

• Gottlieb K. (2016) The FDA adaptive trial design guidance in a nutshell - A review in Q&A format for decision makers. PeerJ Preprints 4:e1825v1 [1]
• Coffey, C. S.; Kairalla, J. A. (2008). "Adaptive clinical trials: Progress and challenges". Drugs in R&D. 9 (4): 229–242. doi:10.2165/00126839-200809040-00003. PMID 18588354. S2CID 11861515.
• Center for Drug Evaluation and Research (CDER); Center for Biologics Evaluation and Research (CBER) (February 2010). "Adaptive Design Clinical Trials for Drugs and Biologics" (PDF). Food and Drug Administration.
• Yi, Cheng; Yu, Shen. "Bayesian Adaptive Designs for Clinical Trials" (PDF). M. D. Anderson.
• Berry, Scott M.; Carlin, Bradley P.; Lee, J. Jack; Muller, Peter (20 July 2010). Bayesian Adaptive Methods for Clinical Trials. CRC Press. ISBN 978-1-4398-2551-8. Berry on BAMCT on YouTube
• Press, W. H. (2009). "Bandit solutions provide unified ethical models for randomized clinical trials and comparative effectiveness research". Proceedings of the National Academy of Sciences. 106 (52): 22387–92. doi:10.1073/pnas.0912378106. PMC 2793317. PMID 20018711.