Response-adaptive randomization for clinical trials with continuous outcomes

Robustness of response-adaptive randomization

Doubly adaptive biased coin design (DBCD), a response-adaptive randomization scheme, aims to skew subject assignment probabilities based on accrued responses for ethical considerations. Recent years have seen substantial advances in understanding DBCD's theoretical properties, assuming correct model specification for the responses. However, concerns have been raised about the impact of model misspecification on its design and analysis. In this paper, we assess the robustness to both design model misspecification and analysis model misspecification under DBCD. On one hand, we confirm that the consistency and asymptotic normality of the allocation proportions can be preserved, even when the responses follow a distribution other than the one imposed by the design model during the implementation of DBCD. On the other hand, we extensively investigate three commonly used linear regression models for estimating and inferring the treatment effect, namely difference-in-means, analysis of covariance (ANCOVA) I, and ANCOVA II. By allowing these regression models to be arbitrarily misspecified, thereby not reflecting the true data generating process, we derive the consistency and asymptotic normality of the treatment effect estimators evaluated from the three models. The asymptotic properties show that the ANCOVA II model, which takes covariate-by-treatment interaction terms into account, yields the most efficient estimator. These results can provide theoretical support for using DBCD in scenarios involving model misspecification, thereby promoting the widespread application of this randomization procedure.

Sample sizes required to estimate the protective efficacy of a vaccine when there is an unequal allocation of individuals across the vaccine and placebo groups

The effectiveness of a vaccine is measured by means of protective vaccine efficacy, defined by V E = 1 - A R V A R U , where A R V and A R U are, respectively, the disease attack rates in the vaccinated and the unvaccinated population. For each of the cohoret and case-control designs, methods have been presented in the literature for calculating the required sample size when the desired width of the confidence interval and the probability of coverage are pre-specified, where an equal number of individuals were assumed to be allocated to the vaccine and placebo group. In this article, we present a method for calculating the required sample size with a specified degree of precision when there is an unequal allocation of individuals across the two groups. The sample size required to achieve a desired power for the relevant level α test has also been explored, keeping the unequal allocation proportion in mind. The fraction of individuals allocated to the placebo group (ρ ) can be so chosen that the total sample size or the expected number of people developing the disease or some other criteria of interest is minimized.

Allogeneic haematopoietic cell transplants as dynamical systems: influence of early-term immune milieu on long-term T-cell recovery

Objectives

Immune recovery following haematopoietic cell transplantation (HCT) functions as a dynamical system. Reducing the duration of intense immune suppression and augmenting antigen presentation has the potential to optimise T-cell reconstitution, potentially influencing long-term outcomes.

Methods

Based on donor-derived T-cell recovery, 26 patients were adaptively randomised between mycophenolate mofetil (MMF) administered for 30-day post-transplant with filgrastim for cytokine support (MMF30 arm, N = 11), or MMF given for 15 days with sargramostim (MMF15 arm, N = 15). All patients underwent in vivo T-cell depletion with 5.1 mg kg^-1 antithymocyte globulin (administered over 3 days, Day -9 through to Day -7) and received reduced intensity 450 cGy total body irradiation (3 fractions on Day -1 and Day 0). Patients underwent HLA-matched related and unrelated donor haematopoietic cell transplantation (HCT).

Results

Clinical outcomes were equivalent between the two groups. The MMF15 arm demonstrated superior T-cell, as well as T-cell subset recovery and a trend towards superior T-cell receptor (TCR) diversity in the first month with this difference persisting through the first year. T-cell repertoire recovery was more rapid and sustained, as well as more diverse in the MMF15 arm.

Conclusion

The long-term superior immune recovery in the MMF15 arm, administered GMCSF, is consistent with a disproportionate impact of early interventions in HCT. Modifying the 'immune-milieu' following allogeneic HCT is feasible and may influence long-term T-cell recovery.

Response-adaptive randomization in clinical trials: from myths to practical considerations

Response-Adaptive Randomization (RAR) is part of a wider class of data-dependent sampling algorithms, for which clinical trials are typically used as a motivating application. In that context, patient allocation to treatments is determined by randomization probabilities that change based on the accrued response data in order to achieve experimental goals. RAR has received abundant theoretical attention from the biostatistical literature since the 1930's and has been the subject of numerous debates. In the last decade, it has received renewed consideration from the applied and methodological communities, driven by well-known practical examples and its widespread use in machine learning. Papers on the subject present different views on its usefulness, and these are not easy to reconcile. This work aims to address this gap by providing a unified, broad and fresh review of methodological and practical issues to consider when debating the use of RAR in clinical trials.

Some performance considerations when using multi-armed bandit algorithms in the presence of missing data

When comparing the performance of multi-armed bandit algorithms, the potential impact of missing data is often overlooked. In practice, it also affects their implementation where the simplest approach to overcome this is to continue to sample according to the original bandit algorithm, ignoring missing outcomes. We investigate the impact on performance of this approach to deal with missing data for several bandit algorithms through an extensive simulation study assuming the rewards are missing at random. We focus on two-armed bandit algorithms with binary outcomes in the context of patient allocation for clinical trials with relatively small sample sizes. However, our results apply to other applications of bandit algorithms where missing data is expected to occur. We assess the resulting operating characteristics, including the expected reward. Different probabilities of missingness in both arms are considered. The key finding of our work is that when using the simplest strategy of ignoring missing data, the impact on the expected performance of multi-armed bandit strategies varies according to the way these strategies balance the exploration-exploitation trade-off. Algorithms that are geared towards exploration continue to assign samples to the arm with more missing responses (which being perceived as the arm with less observed information is deemed more appealing by the algorithm than it would otherwise be). In contrast, algorithms that are geared towards exploitation would rapidly assign a high value to samples from the arms with a current high mean irrespective of the level observations per arm. Furthermore, for algorithms focusing more on exploration, we illustrate that the problem of missing responses can be alleviated using a simple mean imputation approach.

Evaluating response-adaptive randomization procedures for recurrent events and terminal event data using a composite endpoint

Recurrent event and terminal event data commonly arise in clinical and observational studies. To evaluate the efficacy of a treatment effect for both types of events, a composite endpoint has been used as a possible assessment, particularly when faced with high costs and a longer follow-up study. To model recurrent event processes complicated by the existence of a terminal event, joint frailty modeling has been typically employed. In this study, the objective was to develop some target-driven response adaptive randomization strategies using a composite endpoint based on joint frailty modeling. We first implemented a balanced randomized design and then investigated the response adaptive randomization. The former is intuitively first adopted while the latter is expected to be desirable and ethical in terms of allocating more subjects to the more effective treatment. The results show that the proposed procedures using a composite endpoint are capable of reducing the number of trial participants who receive inferior treatment while simultaneously reaching a desired optimal target as compared to a balanced randomized design. The R shiny application for calculating the sample size and allocation probabilities is also available. Finally, two clinical trials were used as pilot datasets to introduce the proposed procedures.

Response-adaptive treatment randomization for multiple comparisons of treatments with recurrentevent responses

Recurrent event responses are frequently encountered during clinical trials of treatments for certain diseases, such as asthma. The recurrence rates of different treatments are often compared by applying the negative binomial model. In addition, a balanced treatment-allocation procedure that assigns the same number of patients to each treatment is often applied. Recently, a response-adaptive treatment-allocation procedure has been developed for trials with recurrent event data, and has been shown to be superior to balanced treatment allocation. However, this response-adaptive treatment allocation procedure is only applicable for the comparison of two treatments. In this paper, we derive response-adaptive treatment-allocation procedures for trials which comprise several treatments. As pairwise comparisons and multiple comparisons with a control are two common multiple-testing scenarios in trials with more than two treatments, corresponding treatment-allocation procedures for these scenarios are also investigated. The redesign of two clinical studies illustrates the clinical benefits that would be obtained from our proposed response-adaptive treatment-allocation procedures.

A new inferential approach for response-adaptive clinical trials: the variance-stabilized bootstrap

This paper discusses disadvantages and limitations of the available inferential approaches in sequential clinical trials for treatment comparisons managed via response-adaptive randomization. Then, we propose an inferential methodology for response-adaptive designs which, by exploiting a variance stabilizing transformation into a bootstrap framework, is able to overcome the above-mentioned drawbacks, regardless of the chosen allocation procedure as well as the desired target. We derive the theoretical properties of the suggested proposal, showing its superiority with respect to likelihood, randomization and design-based inferential approaches. Several illustrative examples and simulation studies are provided in order to confirm the relevance of our results.

Sample size re-estimation for response-adaptive randomized clinical trials

Clinical trials usually take a period of time to recruit volunteers, and they become a steady accumulation of data. Traditionally, the sample size of a trial is determined in advance and data is collected before analysis proceeds. Over the past decades, many strategies have been proposed and rigorous theoretical groundings have been provided to conduct sample size re-estimation. However, the application of these methodologies has not been well extended to take care of trials with adaptive designs. Therefore, we aim to fill the gap by proposing a sample size re-estimation procedure on response-adaptive randomized trial. For ethical and economical concerns, we use multiple stopping criteria with the allowance of early termination. Statistical inference is studied for the hypothesis testing under doubly-adaptive biased coin design. We also prove that the test statistics for each stage are asymptotic independently normally distributed, though dependency exists between the two stages. We find that under our methods, compared to fixed sample size design and other commonly used randomization procedures: (1) power is increased for all scenarios with adjusted sample size; (2) sample size is reduced up to 40% when underestimating the treatment effect; (3) the duration of trials is shortened. These advantages are evidenced by numerical studies and real examples.

Response-adaptive treatment allocation for clinical studies with recurrent event and terminal event data

In long-term clinical studies, recurrent event data are frequently collected to contrast the efficacy of two different treatments. However, the recurrent event process can be stopped by a terminal event, such as death. For analyzing recurrent event and terminal event data, joint frailty modeling has recently received considerable attention because it makes it possible to study the joint evolution over time of both recurrent and terminal event processes and gives consistent and efficient parameters. For a two-arm clinical trial design based on these data sets, there has been limited research on investigating the balanced design, let alone adaptive treatment allocation. Although equal sample size allocation obtained for both treatments is intuitively first adopted in a trial design, if one treatment is expected to be superior, it may be desirable to allocate more subjects to the effective treatment. In this article, we calculate the required sample size based on restricted randomization and then propose a target response-adaptive randomization procedure for recurrent and terminal event outcomes based on the joint frailty model. A randomization procedure, the doubly adaptive biased coin design that targets some optimal allocations, is implemented. The proposed adaptive treatment allocation schemes have been shown to be capable of reducing the number of trial participants who receive inferior treatment while simultaneously reaching an optimal target, as well as retaining a comparable test power as compared to a restricted randomization design. Finally, two clinical studies, the COAPT trial and the A-HeFT trial, are used to illustrate the advantages of adopting the proposed procedure.

Optimal protamine dosing after cardiopulmonary bypass: The PRODOSE adaptive randomised controlled trial

BACKGROUND

The dose of protamine required following cardiopulmonary bypass (CPB) is often determined by the dose of heparin required pre-CPB, expressed as a fixed ratio. Dosing based on mathematical models of heparin clearance is postulated to improve protamine dosing precision and coagulation. We hypothesised that protamine dosing based on a 2-compartment model would improve thromboelastography (TEG) parameters and reduce the dose of protamine administered, relative to a fixed ratio.

METHODS AND FINDINGS

We undertook a 2-stage, adaptive randomised controlled trial, allocating 228 participants to receive protamine dosed according to a mathematical model of heparin clearance or a fixed ratio of 1 mg of protamine for every 100 IU of heparin required to establish anticoagulation pre-CPB. A planned, blinded interim analysis was undertaken after the recruitment of 50% of the study cohort. Following this, the randomisation ratio was adapted from 1:1 to 1:1.33 to increase recruitment to the superior arm while maintaining study power. At the conclusion of trial recruitment, we had randomised 121 patients to the intervention arm and 107 patients to the control arm. The primary endpoint was kaolin TEG r-time measured 3 minutes after protamine administration at the end of CPB. Secondary endpoints included ratio of kaolin TEG r-time pre-CPB to the same metric following protamine administration, requirement for allogeneic red cell transfusion, intercostal catheter drainage at 4 hours postoperatively, and the requirement for reoperation due to bleeding. The trial was listed on a clinical trial registry (ClinicalTrials.gov Identifier: NCT03532594). Participants were recruited between April 2018 and August 2019. Those in the intervention/model group had a shorter mean kaolin r-time (6.58 [SD 2.50] vs. 8.08 [SD 3.98] minutes; p = 0.0016) post-CPB. The post-protamine thromboelastogram of the model group was closer to pre-CPB parameters (median pre-CPB to post-protamine kaolin r-time ratio 0.96 [IQR 0.78-1.14] vs. 0.75 [IQR 0.57-0.99]; p < 0.001). We found no evidence of a difference in median mediastinal/pleural drainage at 4 hours postoperatively (140 [IQR 75-245] vs. 135 [IQR 94-222] mL; p = 0.85) or requirement (as a binary outcome) for packed red blood cell transfusion at 24 hours postoperatively (19 [15.8%] vs. 14 [13.1%] p = 0.69). Those in the model group had a lower median protamine dose (180 [IQR 160-210] vs. 280 [IQR 250-300] mg; p < 0.001). Important limitations of this study include an unblinded design and lack of generalisability to certain populations deliberately excluded from the study (specifically children, patients with a total body weight >120 kg, and patients requiring therapeutic hypothermia to <28°C).

CONCLUSIONS

Using a mathematical model to guide protamine dosing in patients following CPB improved TEG r-time and reduced the dose administered relative to a fixed ratio. No differences were detected in postoperative mediastinal/pleural drainage or red blood cell transfusion requirement in our cohort of low-risk patients.

TRIAL REGISTRATION

ClinicalTrials.gov Unique identifier NCT03532594.

A simple solution to the inadequacy of asymptotic likelihood-based inference for response-adaptive clinical trials

The present paper discusses drawbacks and limitations of likelihood-based inference in sequential clinical trials for treatment comparisons managed via Response-Adaptive Randomization. Taking into account the most common statistical models for the primary outcome—namely binary, Poisson, exponential and normal data—we derive the conditions under which (i) the classical confidence intervals degenerate and (ii) the Wald test becomes inconsistent and strongly affected by the nuisance parameters, also displaying a non monotonic power. To overcome these drawbacks, we provide a very simple solution that could preserve the fundamental properties of likelihood-based inference. Several illustrative examples and simulation studies are presented in order to confirm the relevance of our results and provide some practical recommendations.

A natural lead-in approach to response-adaptive allocation for continuous outcomes

Response-adaptive (RA) allocation designs can skew the allocation of incoming subjects toward the better performing treatment group based on the previously accrued responses. While unstable estimators and increased variability can adversely affect adaptation in early trial stages, Bayesian methods can be implemented with decreasingly informative priors (DIP) to overcome these difficulties. DIPs have been previously used for binary outcomes to constrain adaptation early in the trial, yet gradually increase adaptation as subjects accrue. We extend the DIP approach to RA designs for continuous outcomes, primarily in the normal conjugate family by functionalizing the prior effective sample size to equal the unobserved sample size. We compare this effective sample size DIP approach to other DIP formulations. Further, we considered various allocation equations and assessed their behavior utilizing DIPs. Simulated clinical trials comparing the behavior of these approaches with traditional Frequentist and Bayesian RA as well as balanced designs show that the natural lead-in approaches maintain improved treatment with lower variability and greater power.

Sequential Monte Carlo for response adaptive randomized trials

Response adaptive randomized clinical trials have gained popularity due to their flexibility for adjusting design components, including arm allocation probabilities, at any point in the trial according to the intermediate results. In the Bayesian framework, allocation probabilities to different treatment arms are commonly defined as functionals of the posterior distributions of parameters of the outcome distribution for each treatment. In a non-conjugate model, however, repeated updates of the posterior distribution can be computationally intensive. In this article, we propose an adaptation of sequential Monte Carlo for efficiently updating the posterior distribution of parameters as new outcomes are observed in a general adaptive trial design. An efficient computational tool facilitates implementation of more flexible designs with more frequent interim looks that can in turn reduce the required sample size and expected number of failures in clinical trials. Moreover, more complex statistical models that reflect realistic modeling assumptions can be used for analysis of trial results.

A response-adaptive randomization procedure for multi-armed clinical trials with normally distributed outcomes

We propose a novel response‐adaptive randomization procedure for multi‐armed trials with continuous outcomes that are assumed to be normally distributed. Our proposed rule is non‐myopic, and oriented toward a patient benefit objective, yet maintains computational feasibility. We derive our response‐adaptive algorithm based on the Gittins index for the multi‐armed bandit problem, as a modification of the method first introduced in Villar et al. (Biometrics, 71, pp. 969‐978). The resulting procedure can be implemented under the assumption of both known or unknown variance. We illustrate the proposed procedure by simulations in the context of phase II cancer trials. Our results show that, in a multi‐armed setting, there are efficiency and patient benefit gains of using a response‐adaptive allocation procedure with a continuous endpoint instead of a binary one. These gains persist even if an anticipated low rate of missing data due to deaths, dropouts, or complete responses is imputed online through a procedure first introduced in this paper. Additionally, we discuss how there are response‐adaptive designs that outperform the traditional equal randomized design both in terms of efficiency and patient benefit measures in the multi‐armed trial context.

Response-adaptive treatment allocation for clinical studies with ordinal responses

Ordinal responses are common in clinical studies. Although the proportional odds model is a popular option for analyzing ordered-categorical data, it cannot control the type I error rate when the proportional odds assumption fails to hold. The latent Weibull model was recently shown to be a superior candidate for modeling ordinal data, with remarkably better performance than the latent normal model when the data are highly skewed. In clinical trials with ordinal responses, a balanced design is common, with equal sample allocation for each treatment. However, a more ethical approach is to adopt a response-adaptive allocation scheme in which more patients receive the better treatment. In this paper, we propose the use of the doubly adaptive biased coin design to generate treatment allocations that benefit the trial participants. The proposed treatment allocation scheme not only allows more patients to receive the better treatment, it also maintains compatible test power for the comparison of treatment efficiencies. A clinical example is used to illustrate the proposed procedure.

Optimal designs for testing hypothesis in multiarm clinical trials

The present paper deals with the problem of designing randomized multiarm clinical trials for treatment comparisons in order to achieve a suitable trade-off among inferential precision and ethical concerns. Although the large majority of the literature is focused on the estimation of the treatment effects, in particular for the case of two treatments with binary outcomes, the present paper takes into account the inferential goal of maximizing the power of statistical tests to detect correct conclusions about the treatment effects for normally response trials. After discussing the allocation optimizing the power of the classical multivariate test of homogeneity, we suggest a multipurpose design methodology, based on constrained optimization, which maximizes the power of the test under a suitable ethical constraint reflecting the effectiveness of the treatments. The ensuing optimal allocation depends in general on the unknown model parameters but, contrary to the unconstrained optimal solution or to some targets proposed in the literature, it is a non-degenerate continuous function of the treatment contrasts, and therefore it can be approached by standard response-adaptive randomization procedures. The properties of this constrained optimal allocation are described both theoretically and through suitable examples, showing good performances both in terms of ethical gain and statistical efficiency, taking into account estimation precision as well.

Nonparametric response-adaptive randomization for continuous responses

Many response-adaptive randomization procedures have been proposed and studied over the past few decades. However, most of these procedures are based on parametric structure and do not directly apply to nonparametric models. In this paper, we propose a response-adaptive randomization procedure based on Mann-Whitney U test statistic. Under widely satisfied conditions, we derive asymptotic properties of the randomization procedure and further obtain power functions in form under Mann-Whitney U test. Simulations show the proposed procedure is more robust and more ethical than classical response-adaptive randomization procedures in some circumstances. Advantages of the procedure are also illustrated in a redesigned real clinical trial.

Response-adaptive treatment allocation for survival trials with clustered right-censored data

A comparison of 2 treatments with survival outcomes in a clinical study may require treatment randomization on clusters of multiple units with correlated responses. For example, for patients with otitis media in both ears, a specific treatment is normally given to a single patient, and hence, the 2 ears constitute a cluster. Statistical procedures are available for comparison of treatment efficacies. The conventional approach for treatment allocation is the adoption of a balanced design, in which half of the patients are assigned to each treatment arm. However, considering the increasing acceptability of responsive-adaptive designs in recent years because of their desirable features, we have developed a response-adaptive treatment allocation scheme for survival trials with clustered data. The proposed treatment allocation scheme is superior to the balanced design in that it allows more patients to receive the better treatment. At the same time, the test power for comparing treatment efficacies using our treatment allocation scheme remains highly competitive. The advantage of the proposed randomization procedure is supported by a simulation study and the redesign of a clinical study.

Implementing Optimal Allocation in Clinical Trials with Multiple Endpoints

Modern clinical trials are often complex, with multiple competing objectives and multiple endpoints. Such trials should be both ethical and efficient. In this paper, we overcome the obstacles introduced by the large number of unknown parameters and the possible correlations between the multiple endpoints. We obtain the optimal allocation proportions for the following two optimization problems: (1) maximizing the power of the test of homogeneity with a fixed sample size, and (2) minimizing the expected weighted number of failures with a fixed power. Further, we implement these optimal allocations through response-adaptive randomization procedures. Our theoretical results provide the foundation for the implementation and further investigation of the procedure, and our numerical studies demonstrate its ability to achieve diverse objectives.

Adaptive Designs for Non-inferiority Trials with Multiple Experimental Treatments

The increase in the popularity of non-inferiority clinical trials represents the increasing need to search for substitutes for some reference (standard) treatments. A new treatment would be preferred to the standard treatment if the benefits of adopting it outweigh a possible clinically insignificant reduction in treatment efficacy (non-inferiority margin). Statistical procedures have recently been developed for treatment comparisons in non-inferiority clinical trials that have multiple experimental (new) treatments. An ethical concern for non-inferiority trials is that some patients undergo the less effective treatments; this problem is more serious when multiple experimental treatments are included in a balanced trial in which the sample sizes are the same for all experimental treatments. With the aim of giving fewer patients the inferior treatments, we propose a response-adaptive treatment allocation scheme that is based on the doubly adaptive biased coin design. The proposed adaptive design is also shown to be superior to the balanced design in terms of testing power.

Is the classical Wald test always suitable under response-adaptive randomization?

The aim of this paper is to analyze the impact of response-adaptive randomization rules for normal response trials intended to test the superiority of one of two available treatments. Taking into account the classical Wald test, we show how response-adaptive methodology could induce a consistent loss of inferential precision. Then, we suggest a modified version of the Wald test which, by using the current allocation proportion to the treatments as a consistent estimator of the target, avoids some degenerate scenarios and so it should be preferable to the classical test. Furthermore, we show both analytically and via simulations how some target allocations may induce a locally decreasing power function. Thus, we derive the conditions on the target guaranteeing its monotonicity and we show how a correct choice of the initial sample size allows one to overcome this drawback regardless of the adopted target.

Alternative designs for clinical trials in rare diseases

Evidence-based medicine requires strong scientific evidence upon which to base treatment. In rare diseases, study populations are often small, and thus this evidence is difficult to accrue. Investigators, though, should be creative and develop a flexible toolkit of methods to deal with the problems inherent in the study of rare disease. This narrative review presents alternative clinical trial designs for studying treatments of rare diseases, including cross-over and n-of-1 trials, randomized placebo-phase design, enriched enrollment, randomized withdrawal design, and classes of adaptive designs. Examples of applications of these designs are presented along with their advantages and disadvantages. Additional analytical considerations such as Bayesian analysis, internal pilots, and use of biomarkers as surrogate outcomes are further discussed. A framework for selecting appropriate clinical trial design is proposed to guide investigators in the process of selecting alternative designs for rare diseases. © 2016 Wiley Periodicals, Inc.

The Method of Randomization for Cluster-Randomized Trials: Challenges of Including Patients with Multiple Chronic Conditions

Cluster-randomized clinical trials (CRT) are trials in which the unit of randomization is not a participant but a group (e.g. healthcare systems or community centers). They are suitable when the intervention applies naturally to the cluster (e.g. healthcare policy); when lack of independence among participants may occur (e.g. nursing home hygiene); or when it is most ethical to apply an intervention to all within a group (e.g. school-level immunization). Because participants in the same cluster receive the same intervention, CRT may approximate clinical practice, and may produce generalizable findings. However, when not properly designed or interpreted, CRT may induce biased results. CRT designs have features that add complexity to statistical estimation and inference. Chief among these is the cluster-level correlation in response measurements induced by the randomization. A critical consideration is the experimental unit of inference; often it is desirable to consider intervention effects at the level of the individual rather than the cluster. Finally, given that the number of clusters available may be limited, simple forms of randomization may not achieve balance between intervention and control arms at either the cluster- or participant-level. In non-clustered clinical trials, balance of key factors may be easier to achieve because the sample can be homogenous by exclusion of participants with multiple chronic conditions (MCC). CRTs, which are often pragmatic, may eschew such restrictions. Failure to account for imbalance may induce bias and reducing validity. This article focuses on the complexities of randomization in the design of CRTs, such as the inclusion of patients with MCC, and imbalances in covariate factors across clusters.

A Unified Family of Covariate-Adjusted Response-Adaptive Designs Based on Efficiency and Ethics

Response-adaptive designs have recently attracted more and more attention in the literature because of its advantages in efficiency and medical ethics. To develop personalized medicine, covariate information plays an important role in both design and analysis of clinical trials. A challenge is how to incorporate covariate information in response-adaptive designs while considering issues of both efficiency and medical ethics. To address this problem, we propose a new and unified family of covariate-adjusted response-adaptive (CARA) designs based on two general measurements of efficiency and ethics. Important properties (including asymptotic properties) of the proposed procedures are studied under categorical covariates. This new family of designs not only introduces new desirable CARA designs, but also unifies several important designs in the literature. We demonstrate the proposed procedures through examples, simulations, and a discussion of related earlier work.

A surrogate-primary replacement algorithm for response-adaptive randomization in stroke clinical trials

Response-adaptive randomization (RAR) offers clinical investigators benefit by modifying the treatment allocation probabilities to optimize the ethical, operational, or statistical performance of the trial. Delayed primary outcomes and their effect on RAR have been studied in the literature; however, the incorporation of surrogate outcomes has not been fully addressed. We explore the benefits and limitations of surrogate outcome utilization in RAR in the context of acute stroke clinical trials. We propose a novel surrogate-primary (S-P) replacement algorithm where a patient's surrogate outcome is used in the RAR algorithm only until their primary outcome becomes available to replace it. Computer simulations investigate the effect of both the delay in obtaining the primary outcome and the underlying surrogate and primary outcome distributional discrepancies on complete randomization, standard RAR and the S-P replacement algorithm methods. Results show that when the primary outcome is delayed, the S-P replacement algorithm reduces the variability of the treatment allocation probabilities and achieves stabilization sooner. Additionally, the S-P replacement algorithm benefit proved to be robust in that it preserved power and reduced the expected number of failures across a variety of scenarios.

Shift-invariant target in allocation problems

We provide a template for finding target allocation proportions in optimal allocation designs where the target will be invariant for both shifts in location and scale of the response distributions. One possible application of such target allocation proportions is to carry out a response-adaptive allocation. While most of the existing designs are invariant for any change in scale of the underlying distributions, they are not location invariant in most of the cases. First, we indicate this serious flaw in the existing literature and illustrate how this lack of location invariance makes the performance of the designs very poor in terms of allocation for any drastic change in location, such as the changes from degrees centigrade to degrees Fahrenheit. We illustrate that unless a target allocation is location invariant, it might lead to a completely irrelevant and useless target for allocation. Then we discuss how such location invariance can be achieved for general continuous responses. We illustrate the proposed method using some real clinical trial data. We also indicate the possible extension of the procedure for more than two treatments at hand and in the presence of covariates.

Response-Adaptive Allocation for Circular Data

Response-adaptive designs are used in phase III clinical trials to allocate a larger proportion of patients to the better treatment. Circular data is a natural outcome in many clinical trial setup, e.g., some measurements in opthalmologic studies, degrees of rotation of hand or waist, etc. There is no available work on response-adaptive designs for circular data. With reference to a dataset on cataract surgery we provide some response-adaptive designs where the responses are of circular nature and propose some test statistics for treatment comparison under adaptive data allocation procedure. Detailed simulation study and the analysis of the dataset, including redesigning the cataract surgery data, are carried out.

Traditional and innovative experimental and clinical trial designs and their advantages and pitfalls

Many study designs and design variants have been developed in the past to either overcome or enhance drug-placebo differences in clinical trials or to identify and characterize placebo responders in experimental studies. They share many commonalities as well as differences that are discussed here: the role of deception and ethical restrictions, habituation effects and the control of the natural course of disease, assay sensitivity testing and effective blinding, acceptability and motivation of patients and volunteers, and the development of individualized medicine. These are fostered by two opposite strategies: utilizing the beneficial aspects of the placebo response-and avoiding its negative counterpart, the nocebo effect-in medical routine for the benefit of patients, and minimizing-by controlling-the negative aspects of the placebo effect during drug development.

Adaptive randomization for clinical trials

In February 2010, the U.S. Food and Drug Administration (FDA, 2010 ) drafted guidance that discusses the statistical, clinical, and regulatory aspects of various adaptive designs for clinical trials. An important class of adaptive designs is adaptive randomization, which is considered very briefly in subsection VI.B of the guidance. The objective of this paper is to review several important new classes of adaptive randomization procedures and convey information on the recent developments in the literature on this topic. Much of this literature has been focused on the development of methodology to address past criticisms and concerns that have hindered the broader use of adaptive randomization. We conclude that adaptive randomization is a very broad area of experimental design that has important application in modern clinical trials.

Response-adaptive designs for continuous treatment responses in phase III clinical trials: A review

A variety of response-adaptive randomization procedures have been proposed in literature assuming binary outcomes. However, the list is not so long for continuous outcomes though many real clinical trials deal with continuous treatment responses. In this paper, we attempt to explore the available procedures together with a comparison of their performances. Some real-life adaptive trial is also reviewed.