Response-adaptive Designs Research Articles

A general strategy for reducing the variability in response-adaptive designs

. Adaptive designs for clinical trials address the ethical requirement of allocating fewer patients to the inferior treatment as the trial progresses. We present here a generic strategy partly motivated by play-the-winner heuristic for reducing the variability of such designs. It is shown through simulations from a real clinical setting that employing this strategy improves the safety and the performance of some standard response adaptive designs. A closely related alternate strategy amenable to a theoretical analysis is also proposed with the same objective of lower variability.

StratosPHere 2: study protocol for a response-adaptive randomised placebo-controlled phase II trial to evaluate hydroxychloroquine and phenylbutyrate in pulmonary arterial hypertension caused by mutations in BMPR2

BackgroundPulmonary arterial hypertension is a life-threatening progressive disorder characterised by high blood pressure (hypertension) in the arteries of the lungs (pulmonary artery). Although treatable, there is no known cure for this rare disorder, and its exact cause is unknown. Mutations in the bone morphogenetic protein receptor type-2 (BMPR2) are the most common genetic cause of familial pulmonary arterial hypertension. This study represents the first-ever trial of treatments aimed at directly rescuing the BMPR2 pathway, repurposing two drugs that have shown promise at restoring levels of BMPR2 signalling: hydroxychloroquine and phenylbutyrate.MethodsThis three-armed phase II precision medicine study will investigate BMPR2 target engagement and explore the efficacy of two repurposed therapies in pulmonary arterial hypertension patients with BMPR2 mutations. Patients will be stratified based on two BMPR2 mutation classes: missense and haploinsufficient mutations. Eligible subjects will be randomised to one of the three arms (two active therapy arms and a placebo arm, all plus standard of care) following a Bayesian response-adaptive design implemented independently in each stratum and updated in response to a novel panel of primary biomarkers designed to assess biological modification of the disease.DiscussionThe results of this trial will provide the first randomised evidence of the efficacy of these therapies to rescue BMPR2 function and will efficiently explore the potential for a differential response of these therapies per mutation class to address causes rather than consequences of this rare disease.Trial registrationThe study has been registered with ISRCTN (ISRCTN10304915, 22/09/2023).

Group Response-Adaptive Randomization With Delayed and Missing Responses.

Response-adaptive randomization (RAR) procedures have been extensively studied in the literature, but most of the procedures rely on updating therandomization after each response, which is impractical in many clinical trials. In this article, we propose a new family of RAR procedures that dynamically update based on the responses of a group of individuals, either when available or at fixed time intervals (weekly or biweekly). We show that the proposed design retains the essential theoretical properties of Hu and Zhang's doubly adaptive biased coin designs (DBCD), and performs well in scenarios involving delayed and randomly missing responses. Numerical studies have been conducted to demonstrate that the new proposed group doubly adaptive biased coin design has similar properties to the Hu and Zhang's DBCDs in different situations. We also apply the new design to a real clinical trial, highlighting its advantages and practicality. Our findings open the door to studying the properties of other group response adaptive designs, such as urn models, and facilitate the application of response-adaptive randomized clinical trials in practice.

Incorporating treatment-covariate interaction in designing a covariate adjusted response adaptive allocation for binary response trials

Assuming binary treatment outcomes, a covariate adjusted response adaptive (CARA) design is developed for multi-arm clinical trials assuming the presence of treatment-covariate interaction. The proposed allocation skews the allocation dynamically according to the performance of the treatments and the covariate profile of any incoming subject. Relevant de- sign and precision based measures of the performance are further investigated empirically, which established the usefulness of the proposed allocation design over its competitors. Journal of Statistical Research 2022, Vol. 57, No. 1-2, pp.21-33

Leveraging machine learning: Covariate-adjusted Bayesian adaptive randomization and subgroup discovery in multi-arm survival trials

Clinical trials evaluate the safety and efficacy of treatments for specific diseases. Ensuring these studies are well-powered is crucial for identifying superior treatments. With the rise of personalized medicine, treatment efficacy may vary based on biomarker profiles. However, researchers often lack prior knowledge about which biomarkers are linked to varied treatment effects. Fixed or response-adaptive designs may not sufficiently account for heterogeneous patient characteristics, such as genetic diversity, potentially reducing the chance of selecting the optimal treatment for individuals. Recent advances in Bayesian nonparametric modeling pave the way for innovative trial designs that not only maintain robust power but also offer the flexibility to identify subgroups deriving greater benefits from specific treatments. Building on this inspiration, we introduce a Bayesian adaptive design for multi-arm trials focusing on time-to-event endpoints. We introduce a covariate-adjusted response adaptive randomization, updating treatment allocation probabilities grounded on causal effect estimates using a random intercept accelerated failure time BART model. After the trial concludes, we suggest employing a multi-response decision tree to pinpoint subgroups with varying treatment impacts. The performance of our design is then assessed via comprehensive simulations.

Fixed width confidence interval estimation for general continuous responses under a response adaptive design

. A fixed width confidence interval for the treatment difference is obtained under heteroscedastic normal responses following a response adaptive allocation targeting Neyman optimum allocation. The developed procedure is further extended for general location-scale model based responses under a compound response adaptive allocation. The developed procedures are studied theoretically and are supported empirically assuming various response models. A real data study is also provided following the proposed procedure for the purpose of illustration.

Uncertainty directed factorial clinical trials.

The development and evaluation of novel treatment combinations is a key component of modern clinical research. The primary goals of factorial clinical trials of treatment combinations range from the estimation of intervention-specific effects, or the discovery of potential synergies, to the identification of combinations with the highest response probabilities. Most factorial studies use balanced or block randomization, with an equal number of patients assigned to each treatment combination, irrespective of the specific goals of the trial. Here, we introduce a class of Bayesian response-adaptive designs for factorial clinical trials with binary outcomes. The study design was developed using Bayesian decision-theoretic arguments and adapts the randomization probabilities to treatment combinations during the enrollment period based on the available data. Our approach enables the investigator to specify a utility function representative of the aims of the trial, and the Bayesian response-adaptive randomization algorithm aims to maximize this utility function. We considered several utility functions and factorial designs tailored to them. Then, we conducted a comparative simulation study to illustrate relevant differences of key operating characteristics across the resulting designs. We also investigated the asymptotic behavior of the proposed adaptive designs. We also used data summaries from three recent factorial trials in perioperative care, smoking cessation, and infectious disease prevention to define realistic simulation scenarios and illustrate advantages of the introduced trial designs compared to other study designs.

Bayesian response adaptive randomization design with a composite endpoint of mortality and morbidity.

Allocating patients to treatment arms during a trial based on the observed responses accumulated up to the decision point, and sequential adaptation of this allocation, could minimize the expected number of failures or maximize total benefits to patients. In this study, we developed a Bayesian response-adaptive randomization (RAR) design targeting the endpoint of organ support-free days (OSFD) for patients admitted to the intensive care units. The OSFD is a mixture of mortality and morbidity assessed by the number of days of free of organ support within a predetermined post-randomization time-window. In the past, researchers treated OSFD as an ordinal outcome variable where the lowest category is death. We propose a novel RAR design for a composite endpoint of mortality and morbidity, for example, OSFD, by using a Bayesian mixture model with a Markov chain Monte Carlo sampling to estimate the posterior probability distribution of OSFD and determine treatment allocation ratios at each interim. Simulations were conducted to compare the performance of our proposed design under various randomization rules and different alpha spending functions. The results show that our RAR design using Bayesian inference allocated more patients to the better performing arm(s) compared to other existing adaptive rules while assuring adequate power and type I error rate control across a range of plausible clinical scenarios.

Network and covariate adjusted response-adaptive design for binary response.

Randomization is a distinguishing feature of clinical trials for unbiased assessment of treatment efficacy. With a growing demand for more flexible and efficient randomization schemes and motivated by the idea of adaptive design, in this article we propose the network and covariate adjusted response-adaptive (NCARA) design that can concurrently manage three challenges: (1) maximizing benefits of a trial by assigning more patients to the superior treatment group randomly; (2) balancing social network ties across treatment arms to eliminate potential network interference; and (3) ensuring balance of important covariates, such as age, gender, and other potential confounders. We conduct simulation with different network structures and a variety of parameter settings. It is observed that the NCARA design outperforms four alternative randomization designs in solving the above-mentioned problems and has comparable power and type I error for detecting true difference between treatment groups. In addition, we conduct real data analysis to implement the new design in two clinical trials. Compared to equal randomization (the original design utilized in the trials), the NCARA design slightly increases power, largely increases the percentage of patients assigned to the better-performing group, and significantly improves network and covariate balances. It is also noted that the advantages of the NCARA design are augmented when the sample size is small and the level of network interference is high. In summary, the proposed NCARA design assists researchers in conducting clinical trials with high-quality and high-efficiency.

Reducing the patients-at-risk (PaR) in a response-adaptive trial: A numerical study

This article investigates the dichotomy between higher statistical power and higher allocation to better treatment in an ethical-optimal response-adaptive design. Although many response-adaptive designs in the literature promise higher allocation to the superior treatment, this is not always guaranteed due to the variability of the designs. A new criterion for evaluating response-adaptive designs, motivated by the value-at-risk measure, is proposed to address this problem. We also provide an illustration of applying this criterion in a real clinical trial.

Two-stage response adaptive randomization designs for multi-arm trials with binary outcome

ABSTRACT In recent years, adaptive randomization methods have gained significant popularity in clinical research and trial design due to their ability to provide both efficiency and flexibility in adjusting the statistical procedures of ongoing clinical trials. For a study to compare multiple treatments, a multi-arm two-stage design could be utilized to select the best treatment from the first stage and further compare that treatment with control in the second stage. The traditional design used equal randomization in both stages. To better utilize the interim results from the first stage, we propose to develop response adaptive randomization two-stage designs for a multi-arm clinical trial with binary outcome. Two allocation methods are considered: (1) an optimal allocation based on a sequential design; (2) the play-the-winner rule. Optimal multi-arm two-stage designs are obtained under three criteria: minimizing the expected number of failures, minimizing the average expected sample size, and minimizing the expected sample size under the null hypothesis. Simulation studies show that the proposed adaptive design based on the play-the-winner rule has good performance. A phase II trial for patients with pancreas adenocarcinoma and a germline BRCA / PALB2 mutation was used to illustrate the application of the proposed response adaptive randomization designs.

An optimal sequential design in ethical allocation with an adaptive interim analysis

The present article provides a distribution-free test procedure for comparing the effectiveness of two competing treatments A and B, say, in a clinical trial. Here, the relative treatment effect is measured by the functional θ = P ( X < Y ) , where X and Y are respectively the response variables corresponding to two treatments, A and B. The treatment comparison is done through a testing of hypothesis regarding θ under a patient-benefit oriented two-stage response-adaptive design with an interim analysis in which an optimal sequential sampling scheme with equal randomisation is adopted in stage 1 and a response-adaptive randomisation is employed in stage 2 with the objective of allocating more patients to more promising treatment. The performance of the proposed procedure is compared with that of various other competitive procedures.

Familywise error rate control for block response-adaptive randomization.

Response-adaptive randomization allows the probabilities of allocating patients to treatments in a clinical trial to change based on the previously observed response data, in order to achieve different experimental goals. One concern over the use of such designs in practice, particularly from a regulatory viewpoint, is controlling the type I error rate. To address this, Robertson and Wason (Biometrics, 2019) proposed methodology that guarantees familywise error rate control for a large class of response-adaptive designs by re-weighting the usual -test statistic. In this article, we propose an improvement of their method that is conceptually simpler, in the context where patients are allocated to the experimental treatment arms in a trial in blocks (i.e. groups) using response-adaptive randomization. We show the modified method guarantees that there will never be negative weights for the contribution of each block of data to the adjusted test statistics, and can also provide a substantial power advantage in practice.

Group-sequential response-adaptive designs for multi-armed trials

Several experimental treatments are often compared with a common control in a clinical trial nowadays. A group-sequential design incorporating response-adaptive randomization can help to increase the probability of receiving a more promising treatment for patients in the trial and to detect a treatment effect early so as to benefit the whole population of interest. With such ethical advantages, the trial design has invoked investigation using the Bayesian approach. In the frequentist approach, the type I error rate of a multi-armed trial may involve two error elements, the inflated error rates caused by multiple treatment comparisons and sequential testing. In this study, a group-sequential global test was considered. By monitoring the response-adaptive design at a continuous information time, calculation of the information time and two optimal response-adaptive sampling rules for multi-armed trials were described. Operating characteristics of the designs were investigated via simulation for censored exponential survival outcomes and using patient data sampled from a four-armed binary trial to demonstrate their practical applicability. Our results showed that, in general, the adaptive designs preserved ethical advantages in terms of reducing the average numbers of patients and failures compared with a group-sequential non-adaptive randomized design, while not adversely affecting the power.

Response adaptive randomization design for a two-stage study with binary response

ABSTRACT Response adaptive randomization has the potential to treat more participants in better treatments in a trial to benefit participants. We propose optimal response adaptive randomization designs for a two-stage study with binary response, having the smallest expected sample size or the fewest expected number of failures. Equal randomization is used in the first stage, and data from the first stage is used to determine the adaptive sample size ratio in the second stage. In the proposed optimal designs, the type I error rate and the statistical power are calculated from the asymptotic normal distributions. The new designs that minimize the expected number of failures have the advantage over the existing optimal randomized designs to substantially reduce the number of failures.

Covariate-adjusted response-adaptive designs for censored survival responses

Covariate-adjusted response-adaptive designs use the available responses to skew treatment allocation in a clinical trial, in favour of the treatment, found at an interim stage of the trial, to be best for a patient’s covariate profile. In this study, such designs are developed for censored Weibull responses. The designs are based on the covariate-adjusted doubly-adaptive biased coin design and the covariate-adjusted efficient randomized-adaptive design. The treatment allocation proportion for these designs, converges empirically to the expected target value. An extensive simulation study of the operating characteristics of these designs demonstrates that they can be considered as suitable alternatives to traditional balanced randomization designs, provided responses related to the primary endpoint are available during the recruitment phase to enable adaptations in the design. An existing clinical trial is redesigned using the proposed methodology to illustrate its implementability in real-life scenario.

A comparison of clinical development pathways to advance tuberculosis regimen development.

Current tuberculosis (TB) regimen development pathways are slow and in urgent need of innovation. We investigated novel phase IIc and seamless phase II/III trials utilizing multi-arm multi-stage and Bayesian response adaptive randomization trial designs to select promising combination regimens in a platform adaptive trial. Clinical trial simulation tools were built using predictive and validated parametric survival models of time to culture conversion (intermediate endpoint) and time to TB-related unfavorable outcome (final endpoint). This integrative clinical trial simulation tool was used to explore and optimize design parameters for aforementioned trial designs. Both multi-arm multi-stage and Bayesian response adaptive randomization designs were able to reliably graduate desirable regimens in ≥ 95% of trial simulations and reliably stop suboptimal regimens in ≥ 90% of trial simulations. Overall, adaptive phase IIc designs reduced patient enrollment by 17% and 25% with multi-arm multi-stage and Bayesian response adaptive randomization designs respectively compared to the conventional sequential approach, while seamless designs reduced study duration by 2.6 and 3.5years respectively (typically ≥ 8.5years for standard sequential approach). In this study, we demonstrate that adaptive trial designs are suitable for TB regimen development, and we provide plausible design parameters for a platform adaptive trial. Ultimately trial design and specification of design parameters will depend on clinical trial objectives. To support decision-making for clinical trial designs in contemporary TB regimen development, we provide a flexible clinical trial simulation tool that can be used to explore and optimize design features and parameters.

Multi-arm covariate adjusted response adaptive designs for ordinal outcome clinical trials.

Covariate adjusted response adaptive designs are developed with ordinal categorical responses for phase III clinical trial involving multiple treatments. Stochastic ordering principle is used to order the treatments according to effectiveness and consequently allocation functions are developed by combining the cumulative odds ratios suitably. The performance of the proposed designs is investigated through relevant exact as well as large sample measures. To investigate the performance in a real situation, a real clinical trial involving lung cancer patients is further redesigned using the proposed allocation design.

Comparison of treatments with ordinal responses in trials with sequential monitoring and response-adaptive randomization.

In clinical trials, comparisons of treatments with ordinal responses are frequently conducted using the proportional odds model. However, the use of this model necessitates the adoption of the proportional odds assumption, which may not be appropriate. In particular, when responses are skewed, the use of the proportional odds model may result in a markedly inflated type I error rate. The latent Weibull distribution has recently been proposed to remedy this problem, and it has been demonstrated to be superior to the proportional odds model, especially when response-adaptive randomization is incorporated. However, there are several drawbacks associated with the latent Weibull model and the previously suggested response-adaptive treatment randomization scheme. In this paper, we propose the modified latent Weibull model to address these issues. Based on the modified latent Weibull model, the original response-adaptive design was also revised. In addition, the group sequential monitoring mechanism was included to enable interim analyses to be performed to determine, during a trial, whether a specific treatment is significantly more effective than another. If so, this will enable the trial to be terminated at a much earlier stage than a trial based on a fixed sample size. We performed a simulation study that clearly demonstrated the merits of our proposed framework. Furthermore, we redesigned a clinical study to further illustrate the advantages of our response-adaptive approach.

Low-Dose Decitabine versus Low-Dose Azacitidine in Lower-Risk MDS

BackgroundThe hypomethylating agents are part of the standard of care in the treatment of myelodysplastic syndromes (MDS), but their role in patients with lower-risk disease is unclear.MethodsWe randomly assigned patients with previously untreated MDS with low/intermediate-1 risk by the International Prognostic Scoring System with a Bayesian response-adaptive design to receive either 20 mg/m2 decitabine daily or 75 mg/m2 azacitidine daily on days 1 to 3 every 28-day cycle.ResultsA total of 113 patients were treated: 73 (65%) with decitabine and 40 (35%) with azacitidine. The overall response rate was 67% and 48% in the decitabine and azacitidine groups, respectively (P=0.042); among 59 patients with baseline transfusion dependency, 19 (32%) reached transfusion independence (decitabine, 16 of 39 [41%]; azacitidine, 3 of 20 [15%]; P=0.039). Of the 19 patients who reached transfusion independence, the median duration of transfusion independency was 22 months. Among 54 patients who were transfusion independent at baseline, 5 patients (9%) became transfusion dependent after therapy. No early death was observed. With a median follow-up of 68 months, the median overall event-free survival and overall survival were 17 months and 33 months, respectively.ConclusionsAttenuated dose treatment of hypomethylating agents in patients with lower-risk MDS can improve outcomes without dose-limiting side effects in a high-risk cohort as defined by the Lower-Risk Prognostic Scoring System. (Funded in part by The University of Texas MD Anderson Cancer Center and others; ClinicalTrials.gov number, NCT01720225.)