Sample Size Re-estimation Research Articles

Dynamic Monitoring of Ongoing Clinical Trials

In late 2019, the U.S. FDA updated a new Guidance for Industry on Adaptive Designs for Clinical Trials of Drugs and Biologics. The guideline encourages developing and implementing innovative trial designs for future clinical trials. Group sequential, sample size re-estimation, and adaptive group sequential procedures have been very useful for improving the efficiency of conducting trials for decades. However, they face some challenges in operational aspects which limited their potentials. In this article, we introduce a procedure that addresses these issues. Our proposed procedure has the capability of providing anytime accessibility of the whole up-to-date data history. The procedure is largely built on the proven adaptive group sequential design theories and combined with advanced eClinical technologies in trial management to elevate the trial design and monitoring to a more dynamic level. The construct of a new trial monitoring screen is illustrated with real trial examples. The advantages of anytime accessibility of data and the monitoring a trial with the whole data history and trend are delineated. Data monitoring committees, sponsors and regulatory authorities are important stakeholders in the discussion of how future clinical trials should be monitored with respect to timing and frequency.

Type I Error Inflation of Blinded Sample Size Re-Estimation in Equivalence Testing

Biosimilar products are a relative newcomer in the pharmaceutical industry. It was only in 2006 that the first biosimilar product, Omnitrope[textregistered] (somatropin), a human growth hormone produced by Sandoz was approved by the European Medicine Agency, followed later the same year by the US Food & Drug Administration, and in 2009 by Pharmaceuticals and Medical Devices Agency in Japan. The clinical component of a biosimilar development borrows extensively from traditional drug development. However, when it comes to clinical study designs, this may not always be applicable. This article investigates Type I error violations that occur when blinded sample size reviews are applied in equivalence testing as used in biosimilar drug development. We give a derivation which explains why such violations are more pronounced in equivalence testing than in the case of superiority testing. In addition, the amount of Type I error inflation is quantified by simulation as well as by some theoretical considerations. Nonnegligible Type I error violations arise when blinded interim reassessments of sample sizes are performed particularly if sample sizes are small, but within the range of what is practically relevant.

A systematic review of the \u201cpromising zone\u201d design

IntroductionSample size calculations require assumptions regarding treatment response and variability. Incorrect assumptions can result in under- or overpowered trials, posing ethical concerns. Sample size re-estimation (SSR) methods investigate the validity of these assumptions and increase the sample size if necessary. The “promising zone” (Mehta and Pocock, Stat Med 30:3267–3284, 2011) concept is appealing to researchers for its design simplicity. However, it is still relatively new in the application and has been a source of controversy.ObjectivesThis research aims to synthesise current approaches and practical implementation of the promising zone design.MethodsThis systematic review comprehensively identifies the reporting of methodological research and of clinical trials using promising zone. Databases were searched according to a pre-specified search strategy, and pearl growing techniques implemented.ResultsThe combined search methods resulted in 270 unique records identified; 171 were included in the review, of which 30 were trials. The median time to the interim analysis was 60% of the original target sample size (IQR 41–73%). Of the 15 completed trials, 7 increased their sample size. Only 21 studies reported the maximum sample size that would be considered, for which the median increase was 50% (IQR 35–100%).ConclusionsPromising zone is being implemented in a range of trials worldwide, albeit in low numbers. Identifying trials using promising zone was difficult due to the lack of reporting of SSR methodology. Even when SSR methodology was reported, some had key interim analysis details missing, and only eight papers provided promising zone ranges.

Adaptive Designs in Clinical Trials

The traditional clinical trial is a rigid channel of drug development process. After successful Phase 1 trials and Phase 2 trial with sufficient efficacy and safety, the drugs goes into Phase 3 trials, where it is compared with a placebo or standard treatment (control). Performing this for each new drug separately need more manpower (patients/participant), money (financial resources), and time. On November 2019, the Food and Drug Administration issued a final guidance for the industries entitled, “Adaptive Designs for Clinical Trials of Drugs and Biologics.” They defined adaptive design as “a clinical trial design that allows for prospectively planned modifications to one or more aspects of the design based on accumulating data from subjects in the trial.” In simple words, adaptive design means a design that permits modifications of “the trial procedures and/or statistical procedures of the trial” after its beginning without compromising the validity and the integrity of a trial. There are nine types of adaptive designs that includes adaptive randomization design, group sequential design, sample size re-estimation design, drop-the-losers design, adaptive dose finding design, adaptive treatment-switching design, biomarker-adaptive design, adaptive-hypotheses design, and adaptive seamless Phase II/III designs which has been explained in detail, in this review article. Although the concept of adaptive design has been there from long before but one of the major disadvantages it faces is the lack of uniformity.

Blinded Sample Size Re-Estimation in Comparative Clinical Trials With Overdispersed Count Data: Incorporation of Misspecification of the Variance Function

In randomized clinical trials to compare overdispersed count data between treatment groups, blinded sample size re-estimation (BSSR) is an effective approach to ensure power control even under possible misspecifications of the variance function on overdispersion specified at the design stage. Based on interim clinical trial data, the existing BSSR methods try to find a more appropriate value of the dispersion parameter for a “fixed” variance function specified at the design stage. In this article, we develop a new BSSR method that allows for modification of the variance function itself. Simulation studies demonstrated a stable power control of the proposed method. An application to a phase III clinical trial in chronic obstructive pulmonary disease also demonstrated effectiveness of the proposed method.

Sample size re-estimation in adaptive enrichment design

Clinical trial participants are often heterogeneous, which is a fundamental problem in the rapidly developing field of precision medicine. Participants heterogeneity causes considerable difficulty in the current phase III trial designs. Adaptive enrichment designs provide a flexible and intuitive solution. At the interim analysis, we enrich the subgroup of trial participants who have a higher likelihood to benefit from the new treatment. However, it is critical to control the level of the test size and maintain adequate power after enrichment of certain subgroup of participants. We develop two adaptive enrichment strategies with sample size re-estimation and verify their feasibility and practicability through extensive simulations and sensitivity analyses. The simulation studies show that the proposed methods can control the overall type I error rate and exhibit competitive improvement in terms of statistical power and expected sample size. The proposed designs are exemplified with a real trial application.

Sample size re-estimation for pivotal clinical trials

It is well known that if the hypothesis test is left unchanged, the Type I error rate may be inflated for sample size re-estimation (SSR) designs. To address this issue, three main approaches have been proposed in the literature: combination test, conditional error and conventional test with sample size increase in the allowable region (AR) only. These three seemingly different approaches are in fact connected. For each combination test, there is a corresponding conditional error function and AR. Designing adaptation rules in this AR with conventional test guarantees the Type I error rate control but at the same time always leads to smaller power comparing to the corresponding combination test (or conditional error) approach. In cases where conventional test is still preferable, step-wise type adaptation rules that do not fully reside in the AR can be alternatively considered.We believe controversies in the statistical community on the efficiency comparisons between group sequential (GS) and SSR design stem partially from the misalignment of performance metrics and conditional versus unconditional evaluations. We advocate summary metrics, such as median, variance or tail probabilities of the sample size in addition to expectation and personalizing efficiency definition for each trial sponsor. Conditional metrics by favorable, promising and unfavorable zones of the interim results provide additional insights and should always be incorporated into the decision-making process.

Personalised health education against health damage of COVID-19 epidemic in the elderly Hungarian population (PROACTIVE-19): protocol of an adaptive randomised controlled clinical trial

BackgroundEarly reports indicate that COVID-19 may require intensive care unit (ICU) admission in 5–26% and overall mortality can rise to 11% of the recognised cases, particularly affecting the elderly. There is a lack of evidence-based targeted pharmacological therapy for its prevention and treatment.We aim to compare the effects of a World Health Organization recommendation-based education and a personalised complex preventive lifestyle intervention package (based on the same WHO recommendation) on the outcomes of the COVID-19.MethodsPROACTIVE-19 is a pragmatic, randomised controlled clinical trial with adaptive “sample size re-estimation” design. Hungarian population over the age of 60 years without confirmed COVID-19 will be approached to participate in a telephone health assessment and lifestyle counselling voluntarily. Volunteers will be randomised into two groups: (A) general health education and (B) personalised health education. Participants will go through questioning and recommendation in 5 fields: (1) mental health, (2) smoking habits, (3) physical activity, (4) dietary habits, and (5) alcohol consumption. Both groups A and B will receive the same line of questioning to assess habits concerning these topics. Assessment will be done weekly during the first month, every second week in the second month, then monthly. The composite primary endpoint will include the rate of ICU admission, hospital admission (longer than 48 h), and mortality in COVID-19-positive cases. The estimated sample size is 3788 subjects per study arm. The planned duration of the follow-up is a minimum of 1 year.DiscussionThese interventions may boost the body’s cardiovascular and pulmonary reserve capacities, leading to improved resistance against the damage caused by COVID-19. Consequently, lifestyle changes can reduce the incidence of life-threatening conditions and attenuate the detrimental effects of the pandemic seriously affecting the older population.Trial registrationThe study has been approved by the Scientific and Research Ethics Committee of the Hungarian Medical Research Council (IV/2428- 2 /2020/EKU) and has been registered at clinicaltrials.gov (NCT04321928) on 25 March 2020.

Blinded continuous information monitoring of recurrent event endpoints with time trends in clinical trials.

Blinded sample size re-estimation and information monitoring based on blinded data has been suggested to mitigate risks due to planning uncertainties regarding nuisance parameters. Motivated by a randomized controlled trial in pediatric multiple sclerosis (MS), a continuous monitoring procedure for overdispersed count data was proposed recently. However, this procedure assumed constant event rates, an assumption often not met in practice. Here we extend the procedure to accommodate time trends in the event rates considering two blinded approaches: (a) the mixture approach modeling the number of events by a mixture of two negative binomial distributions and (b) the lumping approach approximating the marginal distribution of the event counts by a negative binomial distribution. Through simulations the operating characteristics of the proposed procedures are investigated under decreasing event rates. We find that the type I error rate is not inflated relevantly by either of the monitoring procedures, with the exception of strong time dependencies where the procedure assuming constant rates exhibits some inflation. Furthermore, the procedure accommodating time trends has generally favorable power properties compared with the procedure based on constant rates which stops often too late. The proposed method is illustrated by the clinical trial in pediatric MS.

Adaptive design implementation in confirmatory trials: methods, practical considerations and case studies

The rapidly changing drug development landscapes have brought unique challenges to sponsors in designing clinical trials in a faster and more efficient way. With the ability to accelerate development timeline, reduce redundant sample size, and select the right dose and patient population during the clinical trial, adaptive designs help to increase the probability of success of clinical trials and eventually contribute to bringing the promising drugs to patients earlier and fulfilling their unmet medical needs. Although extensive adaptive design methods have been proposed in recent years, a comprehensive review of how to implement adaptive design in the practical confirmatory trials is still lacking. In this paper, we will review the evolving history of adaptive designs, updates of newly released regulatory guidance and emerging practical adaptive designs, including but not limited to sample size re-estimation, seamless design and surrogate endpoint used in the interim analysis. Furthermore, we will discuss the current practice of adaptive design implementation by demonstrating a complex oncology seamless phase 2/3 adaptive design case study. Through this example, we will introduce the critical roles of each cross disciplinary function, communication process and important documents when adaptive designs are implemented in real-world setting.

Non-specific effects of rabies vaccine on the incidence of common infectious disease episodes: study protocol for a randomized controlled trial

BackgroundVaccines may cause non-specific effects (NSEs) on morbidity and mortality through immune-mediated mechanisms that are not explained by the prevention of the targeted disease. Much of the evidence for NSEs comes from observational studies with a high risk of bias, and there is a clear need for new data from randomized controlled trials. Recently, it was proposed that rabies vaccine has protective NSEs in people and in animals. The aim of the proposed study is to determine whether rabies vaccine reduces the incidence rate of episodes of common infectious disease syndromes in a population of veterinary students on the island of St. Kitts.MethodsThe trial design is a single-site, two-arm, parallel-group, participant-blinded, randomized, placebo-controlled, two-sided comparative study, with an internal pilot study for blinded sample size re-estimation. Allocation to study arm is by block randomization stratified by sex within cohort with a 1:1 allocation ratio. The primary study outcome is the number of new weekly episodes of common infectious diseases including respiratory, diarrheal and febrile illnesses. A vaccine immunogenicity ancillary study is planned.DiscussionDemonstration of a non-specific protective effect of rabies vaccine against unrelated respiratory, gastrointestinal and febrile illnesses would provide supportive evidence for the design of similar studies in children in populations with a high burden of these illnesses.Trial registrationClinicalTrials.gov, ID: NCT03656198. Registered on 24 August 2018.

Evaluation of an Adaptive Seamless Design for a Phase II/III Clinical Trial in Recurrent Events Data to Demonstrate Reduction in Number of Acute Exacerbations in Patients With Chronic Obstructive Pulmonary Disease (COPD)

–The aim of this work is to present an adaptive two-stage seamless design for a Phase II/III clinical trial in chronic obstructive pulmonary disease (COPD). This approach implies sample size re-estimation based on the primary outcome efficacy variable, namely the annual acute exacerbation rate. Patient recruitment in COPD trials can be slow; the proposed statistical approach may allow for a faster completion of the trial by reducing the time between interim analysis (IA) (Phase II) and new recruitment (Phase III). In addition, an adaptive design with re-estimation of the sample size during the study may increase its likelihood of success, especially in cases where estimated interim vaccine efficacy is lower than expected. By applying the proposed approach, at the time of the IA (end of Phase II), a decision can be taken to stop the trial for futility, to continue the trial (by advancing to Phase III) with the planned sample size or to continue the study and increase the sample size. We carried out simulations to evaluate the performance of the method.

A phase Ib/III randomized, double-blind, placebo-controlled study of tazemetostat plus doxorubicin as frontline therapy for patients with advanced epithelioid sarcoma.

TPS11573 Background: Epithelioid sarcoma (ES) is characterized by loss of inhibitor of integrase 1 (INI1), allowing enhancer of zeste homologue 2 (EZH2) to repress cell differentiation and promote tumorigenesis. Tazemetostat (TAZ) is an EZH2 inhibitor approved by the FDA as monotherapy for the treatment of patients (pts) aged ≥16 years with metastatic or locally advanced ES ineligible for complete resection. The most common ( > 20%) adverse events include pain, fatigue, nausea, decreased appetite, vomiting, and constipation. TAZ demonstrated anti-cancer activity in ES pts who previously received doxorubicin (dox), a commonly used front line FDA approved therapy for soft tissue sarcomas (STS), including ES. Preclinical studies have demonstrated synergy between TAZ and dox. This phase 1b/3 study (NCT04204941) is assessing the safety and efficacy of TAZ + dox in pts with advanced ES. Methods: This study is enrolling treatment-naïve adult pts with histologically confirmed STS (phase 1b) or INI1-deficient ES pts (phase 3) with ECOG performance status of 0-2 and life expectancy of ≥3 months. The open-label phase 1b portion is currently enrolling up to 24 patients with STS. The primary objectives are to evaluate the safety and tolerability of TAZ + dox, and to identify the recommended phase 3 dose (RP3D). Using a standard 3 + 3 design, TAZ will be evaluated at 3 dose levels (400 mg, 600 mg, and 800 mg orally BID) with standard fixed dose of dox (75 mg/m2 on day 1 of 21-day cycles for up to 6 cycles). After completion of dox, pts will continue to receive TAZ monotherapy until disease progression or intolerable toxicity. Up to 12 additional pts will be enrolled at the maximum tolerated dose for collection of additional safety and pharmacokinetic (PK) data. The double-blind phase 3 study will randomize (1:1) up to 140 pts to receive either TAZ (RP3D) + dox (75 mg/m2) or placebo + dox. The adaptive study design will allow sample size re-estimation (at interim analysis) to up to 200 pts. Pts may continue TAZ or placebo monotherapy after completing 6 cycles of TAZ + dox. Tumor assessments will be performed at screening and every 6 weeks from the start of dosing. The primary objective of phase 3 will be to evaluate the progression-free survival (PFS) by independent review committee. Secondary objectives will include assessment of PFS by investigators, overall survival, safety, disease control rate, overall response rate, duration of response, time to subsequent anticancer therapy, PFS on next treatment, quality of life, and PK. Clinical trial information: NCT04204941 .

Sample size calculation and re-estimation based on the prevalence in a single-arm confirmatory diagnostic accuracy study.

In a confirmatory diagnostic accuracy study, sensitivity and specificity are considered as co-primary endpoints. For the sample size calculation, the prevalence of the target population must be taken into account to obtain a representative sample. In this context, a general problem arises. With a low or high prevalence, the study may be overpowered in one subpopulation. One further issue is the correct pre-specification of the true prevalence. With an incorrect assumption about the prevalence, an over- or underestimated sample size will result. To obtain the desired power independent of the prevalence, a method for an optimal sample size calculation for the comparison of a diagnostic experimental test with a prespecified minimum sensitivity and specificity is proposed. To face the problem of an incorrectly pre-specified prevalence, a blinded one-time re-estimation design of the sample size based on the prevalence and a blinded repeated re-estimation design of the sample size based on the prevalence are evaluated by a simulation study. Both designs are compared to a fixed design and additionally among each other. The type I error rates of both blinded re-estimation designs are not inflated. Their empirical overall power equals the desired theoretical power and both designs offer unbiased estimates of the prevalence. The repeated re-estimation design reveals no advantages concerning the mean squared error of the re-estimated prevalence or sample size compared to the one-time re-estimation design. The appropriate size of the internal pilot study in the one-time re-estimation design is 50% of the initially calculated sample size. A one-time re-estimation design of the prevalence based on the optimal sample size calculation is recommended in single-arm diagnostic accuracy studies.

Blinded sample size reestimation for negative binomial regression with baseline adjustment.

In randomized clinical trials, it is standard to include baseline variables in the primary analysis as covariates, as it is recommended by international guidelines. For the study design to be consistent with the analysis, these variables should also be taken into account when calculating the sample size to appropriately power the trial. Because assumptions made in the sample size calculation are always subject to some degree of uncertainty, a blinded sample size reestimation (BSSR) is recommended to adjust the sample size when necessary. In this article, we introduce a BSSR approach for count data outcomes with baseline covariates. Count outcomes are common in clinical trials and examples include the number of exacerbations in asthma and chronic obstructive pulmonary disease, relapses, and scan lesions in multiple sclerosis and seizures in epilepsy. The introduced methods are based on Wald and likelihood ratio test statistics. The approaches are illustrated by a clinical trial in epilepsy. The BSSR procedures proposed are compared in a Monte Carlo simulation study and shown to yield power values close to the target while not inflating the type I error rate.

Adaptive multiarm multistage clinical trials.

Two methods for designing adaptive multiarm multistage (MAMS) clinical trials, originating from conceptually different group sequential frameworks are presented, and their operating characteristics are compared. In both methods pairwise comparisons are made, stage‐by‐stage, between each treatment arm and a common control arm with the goal of identifying active treatments and dropping inactive ones. At any stage one may alter the future course of the trial through adaptive changes to the prespecified decision rules for treatment selection and sample size reestimation, and notwithstanding such changes, both methods guarantee strong control of the family‐wise error rate. The stage‐wise MAMS approach was historically the first to be developed and remains the standard method for designing inferentially seamless phase 2‐3 clinical trials. In this approach, at each stage, the data from each treatment comparison are summarized by a single multiplicity adjusted P‐value. These stage‐wise P‐values are combined by a prespecified combination function and the resultant test statistic is monitored with respect to the classical two‐arm group sequential efficacy boundaries. The cumulative MAMS approach is a more recent development in which a separate test statistic is constructed for each treatment comparison from the cumulative data at each stage. These statistics are then monitored with respect to multiplicity adjusted group sequential efficacy boundaries. We compared the powers of the two methods for designs with two and three active treatment arms, under commonly utilized decision rules for treatment selection, sample size reestimation and early stopping. In our investigations, which were carried out over a reasonably exhaustive exploration of the parameter space, the cumulative MAMS designs were more powerful than the stage‐wise MAMS designs, except for the homogeneous case of equal treatment effects, where a small power advantage was discernable for the stage‐wise MAMS designs.

A parametric multiple test procedure to adaptive group-sequential trials allowing for mid-term modifications

Adaptive group-sequential trials incorporating mid-term design modifications, such as selection of hypotheses or treatment groups, or sample size reestimation during the interim analysis, provide efficient tools to utilize the data from enrolled patients. A graphical multiple test procedure using weighted Bonferroni test has been proposed in adaptive group-sequential designs based on a marginal p-value combination approach.Multiple test procedures using weighted Bonferroni test may result in potential power loss when the test statistics are positively correlated. This paper proposes a graphical approach for adaptive group-sequential trials using weighted parametric test which utilizes the correlation structure between test statistics. The proposed parametric approach is more powerful than the Bonferroni-based approach and preserves the familywise Type I error. The proposed approach is appealing in adaptive designs with the option of treatment selection during the interim analysis and seamless combination of the dose finding and confirmatory stage, because it can utilize the natural correlations between the test statistics resulting from comparing a few treatment groups to a common control group.Simulations are conducted to evaluate the power and Type I error. The proposed parametric approach is illustrated with a confirmatory two-stage adaptive design with a dose selection stage and confirmatory stage with the selected treatment groups.

A Practical Perspective: Application of the Generalized Approach for Adaptive Design.

Adaptive design methodology has been well studied for continuous, binary, and survival outcomes for decades. However, for complicated endpoints such as recurrent hospitalization in the joint frailty setting and composite endpoint in the win-ratio setting, adaptive design is not intuitive because of sophistication in existing methods to perform sample size re-estimation. The objective of this paper is to propose a practical generalized approach to implement the above activities at the interim stage through approximation so that sample size re-estimation becomes easily understood and readily amenable. Through simulations on representative complex situations, the proposed method can maintain the planned statistical power by sample size re-estimation while controlling the type I error. The proposed adaptive approach is easy to implement in general sample size re-estimation situations. Its validity can be verified through simulation under varying scenarios. In summary, this approach offers a transparent communication channel with regulatory agencies to facilitate clinical trial development regardless of the complexity of the underlying situations.

Adaptive Design: A Review of the Technical, Statistical, and Regulatory Aspects of Implementation in a Clinical Trial.

In an adaptive trial, the researcher may have the option of responding to interim safety and efficacy data in a number of ways, including narrowing the study focus or increasing the number of subjects, balancing treatment allocation or different forms of randomization based on responses of subjects prior to treatment. This research aims at compiling the technical, statistical, and regulatory implications of the employment of adaptive design in a clinical trial. Review of adaptive design clinical trials in Medline, PubMed, EU Clinical Trials Register, and ClinicalTrials.gov. Phase I and seamless phase I/II trials were excluded. We selected variables extracted from trials that included basic study characteristics, adaptive design features, size and use of independent data-monitoring committees (DMCs), and blinded interim analysis. The research retrieved 336 results, from which 78 were selected for analysis. Sixty-seven were published articles, and 11 were guidelines, papers, and regulatory bills. The most prevalent type of adaptation was the seamless phase II/III design 23.1%, followed by adaptive dose progression 19.2%, pick the winner / drop the loser 16.7%, sample size re-estimation 10.3%, change in the study objective 9.0%, adaptive sequential design 9.0%, adaptive randomization 6.4%, biomarker adaptive design 3.8%, and endpoint adaptation 2.6%. Discussion DISCUSSION: It is possible to infer that the use of Adaptive Design is an ethical and scientific advantage when properly planned and applied, since it increases the flexibility of the trial, shortens the overall clinical investigation time of a drug, and reduces the risk of patient exposure to adverse effects related to the experimental drug. Its greater methodologic and analytic complexity requires an adequate statistical methodology. The application of "adaptive clinical designs" for phase II/III studies appear to have been limited to trials with a small number of study centers, with smaller extensions of time and to experimental drugs with more immediate clinical effects that are amenable to risk/benefit decisions based on interim analyses. According to the reviewed studies, simple adaptive trial designs-such as early study terminations due to futility and sample size re-estimation-are becoming widely adopted throughout the pharmaceutical industry, especially in phase II and III studies. The pharmaceutical industry and contract research organizations (CROs) are implementing simple adaptations more frequently and the more complex adaptations-biomarker adaptive design, endpoint adaptation-are more sporadic.

Adaptive Designs: Results of 2016 Survey on Perception and Use.

The DIA Adaptive Designs Scientific Working Group has a devoted subteam to performing surveys, literature reviews, and registry reviews every 4 years to assess the perception and use of adaptive designs (ADs) in the development of drugs and biologics. A survey was distributed to pharmaceutical companies, academic institutions, and contract research organizations to collect information about the usage of ADs of different types and perception of challenges for their use. Literature and registry reviews were conducted to assess the prevalence of ADs of different types in drug and biologics development. These results were compared to previous surveys and reviews using summary statistics. ADs appear to be more widely considered in the last 4 years as compared to earlier 4-year periods. The most common types of ADs remain early stopping, treatment group adaptations, and sample size re-estimation. Both stopping early for safety and changing the endpoint of the analyses were rarely mentioned in literature prior to 2012 but are now appearing more frequently. The barriers of change management and negative experiences by some institutions with ADs remain a source of concern. Additional, consistent training would be helpful to choose the right adaptation(s) needed for specific clinical trials and for planning appropriately for operational efficiency such as for drug supply management and data management. The perceived barrier of regulatory acceptance also remains a concern, which could be alleviated by additional interaction with agencies and an update of the FDA draft guidance to industry on adaptive designs.