In an adaptive design of a clinical trial, the parameters and conduct of the trial for a candidate drug or vaccine may be changed based on an interim analysis. Adaptive design typically involves advanced statistics to interpret a clinical trial endpoint. This is in contrast to traditional single-arm (i.e. non-randomized) clinical trials or randomized clinical trials (RCTs) that are static in their protocol and do not modify any parameters until the trial is completed. The adaptation process takes place at certain points in the trial, prescribed in the trial protocol. Importantly, this trial protocol is set before the trial begins with the adaptation schedule and processes specified. Adaptions may include modifications to: dosage, sample size, drug undergoing trial, patient selection criteria and/or "cocktail" mix. The PANDA (A Practical Adaptive & Novel Designs and Analysis toolkit) provides not only a summary of different adaptive designs, but also comprehensive information on adaptive design planning, conduct, analysis and reporting.
Purpose 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. 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.
History
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.
The FDA issued draft guidance on adaptive trial design in 2010. In 2012, the President's Council of Advisors on Science and Technology (PCAST) recommended that the 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 they "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."
By 2019, the FDA updated their 2010 recommendations and issued "Adaptive Design Clinical Trials for Drugs and Biologics Guidance". In October of 2021, the FDA Center for Veterinary Medicine issued the Guidance Document "Adaptive and Other Innovative Designs for Effectiveness Studies of New Animal Drugs".
Characteristics
Traditionally, clinical trials are conducted in three steps: The trial is designed. The trial is conducted as prescribed by the design. Once the data are ready, they are analysed according to a pre-specified analysis plan.
Types Overview 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: In some cases, trials have become an ongoing process that regularly adds and drops therapies and patient groups as more information is gained. +Trial Design TypeAdaptable ElementDescriptionDose-findingTreatment doseDose may be changed to find minimally toxic, and maximally effective dosing.Adaptive hypothesisTrial endpointsAccording 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. Group sequentialSample 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. This type of design is explained in detail on PANDA.Response adaptive randomisationRandomization ratiosThe chance of being randomized 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. This type of design is explained in detail on PANDA.Adaptive treatment-switchingTreatmentThese trials, based on pre-set rules, can change individual patients from one group to another.Biomarker adaptiveMultiple, on the basis of biomarker discoveriesThese 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 enrichmentPopulation enrolledThe population that the trial enrolls from may change based on, for example, improved epidemiological understanding of a disease. This type of design is explained in detail on PANDA.Platform TrialMultiple, on the basis that all different treatment groups share the same, single control groupPlatform trials are defined by having a constant control group, against which variable treatment groups are compared.Multi-arm multi-stageThe current treatment armsThese trials adapt to stop recruitment to treatment arms that show less efficacy and therefore they do not allocate new participants to the least effective-seeming treatment arms. This type of design is explained in detail on PANDA.Sample size re-estimationSample sizeSample sizes of either the whole trial or individual groups may change as more becomes known about effect sizes. This type of design is explained in detail on PANDA.Seamless Phase I/IIEntry into phase II trialsThese trials collect data on safety and dosing simultaneously.Seamless Phase II/IIIEntry into phase III trialsThese trials collect data on dosing and efficacy simultaneously.
Dose finding design Phase I of clinical research focuses on selecting a particular dose of a drug to carry forward into future trials. Historically, such trials have had a "rules-based" (or "algorithm-based") design, such as the 3+3 design. However, these "A+B" rules-based designs are not appropriate for phase I studies and are inferior to adaptive, model-based designs. An example of a superior design is the continual reassessment method (CRM).
Group sequential design Group sequential design is the application of sequential analysis to clinical trials. At each interim analysis, investigators will use the current data to decide whether the trial should either stop or should continue to recruit more participants. The trial might stop either because the evidence that the treatment is working is strong ("stopping for benefit") or weak ("stopping for futility"). Whether a trial may stop for futility only, benefit only, or either, is stated in advance. A design has "binding stopping rules" when the trial must stop when a particular threshold of (either strong or weak) evidence is crossed at a particular interim analysis. Otherwise it has "non-binding stopping rules", in which case other information can be taken into account, for example safety data. The number of interim analyses is specified in advance, and can be anything from a single interim analysis (a "two-stage" design") to an interim analysis after every participant ("continuous monitoring").
For trials with a binary (response/no response) outcome and a single treatment arm, a popular and simple group sequential design with two stages is the Simon design. In this design, there is a single interim analysis partway through the trial, at which point the trial either stops for futility or continues to the second stage. Mander and Thomson also proposed a design with a single interim analysis, at which point the trial could stop for either futility or benefit.
For single-arm, single-stage binary outcome trials, a trial's success or failure is determined by the number of responses observed by the end of the trial. This means that it may be possible to know the conclusion of the trial (success or failure) with certainty before all the data are available. Planning to stop a trial once the conclusion is known with certainty is called non-stochastic curtailment. This reduces the sample size on average. Planning to stop a trial when the probability of success, based on the results so far, is either above or below a certain threshold is called stochastic curtailment. This reduces the average sample size even more than non-stochastic curtailment. Stochastic and non-stochastic curtailment can also be used in two-arm binary outcome trials, where a trial's success or failure is determined by the number of responses observed on each arm by the end of the trial.
Usage
The adaptive design method developed mainly in the early 21st century. In November 2019, the US Food and Drug Administration provided guidelines for using adaptive designs in clinical trials.
In 2020 COVID-19 related trials
In April 2020, the World Health Organization published an "R&D Blueprint (for the) novel Coronavirus" (Blueprint). The Blueprint documented a "large, international, multi-site, individually randomized controlled clinical trial" to allow "the concurrent evaluation of the benefits and risks of each promising candidate vaccine within 3–6 months of it being made available for the trial." The Blueprint listed a Global Target Product Profile (TPP) for COVID‑19, identifying favorable attributes of safe and effective vaccines under two broad categories: "vaccines for the long-term protection of people at higher risk of COVID-19, such as healthcare workers", and other vaccines to provide rapid-response immunity for new outbreaks.
The international TPP team was formed to: Assess the development of the most promising candidate vaccines. Map candidate vaccines and their clinical trial worldwide, publishing a frequently-updated "landscape" of vaccines in development. Rapidly evaluate and screen for the most promising candidate vaccines simultaneously before they are tested in humans. Design and coordinate a multiple-site, international randomized controlled trialthe "Solidarity trial" for vaccinesto enable simultaneous evaluation of the benefits and risks of different vaccine candidates under clinical trials in countries where there are high rates of COVID‑19 disease, ensuring fast interpretation and sharing of results around the world.
The WHO vaccine coalition prioritized which vaccines would go into PhaseII and III clinical trials, and determined harmonized PhaseIII protocols for all vaccines achieving the pivotal trial stage.
The global Solidarity and European Discovery trials of hospitalized people with severe COVID‑19 infection applied adaptive design to rapidly alter trial parameters as results from the four experimental therapeutic strategies emerged. The US National Institute of Allergy and Infectious Diseases (NIAID) initiated an adaptive design, international Phase III trial named the Adaptive COVID-19 Treatment Trial (ACTT) to involve up to 800 hospitalized COVID‑19 people at 100 sites in multiple countries.
Breast cancer