Using short-term response information to facilitate adaptive randomization for survival clinical trials

A Bayesian adaptive biomarker stratified phase II randomized clinical trial design for radiotherapies with competing risk survival outcomes

In recent decades, many phase II clinical trials have used survival outcomes as the primary endpoints. If radiotherapy is involved, the competing risk issue often arises because the time to disease progression can be censored by the time to normal tissue complications, and vice versa. Besides, many existing research has examined that patients receiving the same radiotherapy dose may yield distinct responses due to their heterogeneous radiation susceptibility statuses. Therefore, the "one-size-fits-all" strategy often fails, and it is more relevant to evaluate the subgroup-specific treatment effect with the subgroup defined by the radiation susceptibility status. In this paper, we propose a Bayesian adaptive biomarker stratified phase II trial design evaluating the subgroup-specific treatment effects of radiotherapy. We use the cause-specific hazard approach to model the competing risk survival outcomes. We propose restricting the candidate radiation doses based on each patient's radiation susceptibility status. Only the clinically feasible personalized dose will be considered, which enhances the benefit for the patients in the trial. In addition, we propose a stratified Bayesian adaptive randomization scheme such that more patients will be randomized to the dose reporting more favorable survival outcomes. Numerical studies and an illustrative trial example have shown that the proposed design performed well and outperformed the conventional design ignoring the competing risk issue.

Bayesian phase II adaptive randomization by jointly modeling efficacy and toxicity as time-to-event outcomes

The main goals of Phase II trials are to identify the therapeutic efficacy of new treatments and continue monitoring all the possible adverse effects. In Phase II trials, it is important to develop an adaptive randomization (AR) procedure that takes into account both the efficacy and toxicity. In most existing articles, toxicity is modeled as a binary endpoint through an unobservable random effect (frailty) to link the efficacy and toxicity. However, this approach does not capture toxicity profiles that evolve over time. In this article, we propose a new Bayesian adaptive randomization (BAR) procedure using the covariate-adjusted efficacy-toxicity ratio (ETR) index, where efficacy and toxicity are jointly modelled as time-to-event (TTE) outcomes. Furthermore, we also propose early stopping rules for toxicity and futility such that inferior treatments can be dropped at earlier time of trial. Simulation results show that compared to the BAR procedures based solely on the efficacy and that based on TTE efficacy and binary toxicity outcomes, the proposed BAR procedure can better identify the difference in treatment toxicity such that it can assign more patients to the superior treatment arm under some scenarios.

Bayesian adaptive design for covariate-adaptive historical control information borrowing

Interest in incorporating historical data in the clinical trial has increased with the rising cost of conducting clinical trials. The intervention arm for the current trial often requires prospective data to assess a novel treatment, and thus borrowing historical control data commensurate in distribution to current control data is motivated in order to increase the allocation ratio to the current intervention arm. Existing historical control borrowing adaptive designs adjust allocation ratios based on the commensurability assessed through study-level summary statistics of the response agnostic of the distributions of the trial subject characteristics in the current and historical trials. This can lead to distributional imbalance of the current trial subject characteristics across the treatment arms as well as between current control data and borrowed historical control data. Such covariate imbalance may threaten the internal validity of the current trial by introducing confounding factors that affect study endpoints. In this article, we propose a Bayesian design which borrows and updates the treatment allocation ratios both covariate-adaptively and commensurate to covariate dependently assessed similarity between the current and historical control data. We employ covariate-dependent discrepancy parameters which are allowed to grow with the sample size and propose a regularized local regression procedure for the estimation of the parameters. The proposed design also permits the current and the historical controls to be similar to varying degree, depending on the subject level characteristics. We evaluate the proposed design extensively under the settings derived from two placebo-controlled randomized trials on vertebral fracture risk in post-menopausal women.

Evolution of Phase II Oncology Trial Design: from Single Arm to Master Protocol

The recent development of novel anticancer treatments with diverse mechanisms of action has accelerated the detection of treatment candidates tremendously. The rapidly changing drug development landscapes and the high failure rates in Phase III trials both underscore the importance of more efficient and robust phase II designs. The goals of phase II oncology studies are to explore the preliminary efficacy and toxicity of the investigational product and to inform future drug development strategies such as go/no-go decisions for phase III development, or dose/indication selection. These complex purposes of phase II oncology designs call for efficient, flexible, and easy-to-implement clinical trial designs. Therefore, innovative adaptive study designs with the potential of improving the efficiency of the study, protecting patients, and improving the quality of information gained from trials have been commonly used in Phase II oncology studies. Although the value of adaptive clinical trial methods in early phase drug development is generally well accepted, there is no comprehensive review and guidance on adaptive design methods and their best practice for phase II oncology trials. In this paper, we review the recent development and evolution of phase II oncology design, including frequentist multistage design, Bayesian continuous monitoring, master protocol design, and innovative design methods for randomized phase II studies. The practical considerations and the implementation of these complex design methods are also discussed.

When is a two-stage single-arm trial efficient? An evaluation of the impact of outcome delay

BACKGROUND

Simon's two-stage design is a widely used adaptive design, particularly in phase II oncology trials due to its simplicity and efficiency. However, its efficiency can be adversely affected when the primary end-point takes time to observe, as is common in practice.

METHODS

We propose an optimal design, taking the delay in observing treatment outcome into consideration and compare the efficiency gained from using Simon's design over a single-stage design for real-life oncology trials. Based on the results, we provide a general rule-of-thumb for determining whether a two-stage single-arm design can provide any added advantage over a single-stage design, given the recruitment rate and primary end-point length.

RESULTS

We observed an average 15-30% loss in the estimated efficiency gain in real oncology trials that used Simon's design due to the delay in observing the treatment outcome. The delay-optimal design provides some advantage over Simon's design in terms of reduced sample size when the delay is large compared to the recruitment length.

DISCUSSION

Simon's two-stage design provides large benefit over a single-stage design, in terms of reduced sample size, when the primary end-point length is no more than 10% of the total recruitment time. It provides no efficiency advantage when this ratio is above 50%.

A Bayesian hierarchical monitoring design for phase II cancer clinical trials: Incorporating information on response duration into monitoring rules

We propose a Bayesian hierarchical monitoring design for single-arm phase II clinical trials of cancer treatments that incorporates the information on the duration of response (DOR) into the monitoring rules. To screen a new treatment by evaluating its preliminary therapeutic effect, futility monitoring rules are commonly used in phase II clinical trials to make "go/no-go" decisions timely and efficiently. These futility monitoring rules are usually focused on a single outcome (eg, response rate), although a single outcome may not adequately determine the efficacy of the experimental treatment. For example, targeted agents with a long response duration but a similar response rate may be worth further evaluation in cancer research. To address this issue, we propose Bayesian hierarchical futility monitoring rules to consider both the response rate and duration. The first level of monitoring evaluates whether the response rate provides evidence that the experimental treatment is worthy of further evaluation. If the evidence from the response rate does not support continuing the trial, the second level monitoring rule, which is based on the DOR, will be triggered. If both stopping rules are satisfied, the trial will be stopped for futility. We conducted simulation studies to evaluate the operating characteristics of the proposed monitoring rules and compared them to those of standard method. We illustrated the proposed design with a single-arm phase II cancer clinical trial to assess the safety and efficacy of combined treatment of nivolumab and azacitidine in patients with relapsed/refractory acute myeloid leukemia. The proposed design avoids an aggressive early termination for futility when the experimental treatment substantially prolongs the DOR but fails to improve the response rate.

Critical appraisal of Bayesian dynamic borrowing from an imperfectly commensurate historical control

Bayesian dynamic borrowing designs facilitate borrowing information from historical studies. Historical data, when perfectly commensurate with current data, have been shown to reduce the trial duration and the sample size, while inflation in the type I error and reduction in the power have been reported, when imperfectly commensurate. These results, however, were obtained without considering that Bayesian designs are calibrated to meet regulatory requirements in practice and even no-borrowing designs may use information from historical data in the calibration. The implicit borrowing of historical data suggests that imperfectly commensurate historical data may similarly impact no-borrowing designs negatively. We will provide a fair appraiser of Bayesian dynamic borrowing and no-borrowing designs. We used a published selective adaptive randomization design and real clinical trial setting and conducted simulation studies under varying degrees of imperfectly commensurate historical control scenarios. The type I error was inflated under the null scenario of no intervention effect, while larger inflation was noted with borrowing. The larger inflation in type I error under the null setting can be offset by the greater probability to stop early correctly under the alternative. Response rates were estimated more precisely and the average sample size was smaller with borrowing. The expected increase in bias with borrowing was noted, but was negligible. Using Bayesian dynamic borrowing designs may improve trial efficiency by stopping trials early correctly and reducing trial length at the small cost of inflated type I error.

Response adaptive randomization procedures in seamless phase II/III clinical trials

It is desirable to work efficiently and cost effectively to evaluate new therapies in a time-sensitive and ethical manner without compromising the integrity and validity of the development process. The seamless phase II/III clinical trial has been proposed to meet this need, and its efficient, ethical and economic advantages can be strengthened by its combination with innovative response adaptive randomization (RAR) procedures. In particular, well-designed frequentist RAR procedures can target theoretically optimal allocation proportions, and there are explicit asymptotic results. However, there has been little research into seamless phase II/III clinical trials with frequentist RAR because of the difficulty in performing valid statistical inference and controlling the type I error rate. In this paper, we propose the framework for a family of frequentist RAR designs for seamless phase II/III trials, derive the asymptotic distribution of the parameter estimators using martingale processes and offer solutions to control the type I error rate. The numerical studies demonstrate our theoretical findings and the advantages of the proposed methods.

Bayesian selective response-adaptive design using the historical control

High quality historical control data, if incorporated, may reduce sample size, trial cost, and duration. A too optimistic use of the data, however, may result in bias under prior-data conflict. Motivated by well-publicized two-arm comparative trials in stroke, we propose a Bayesian design that both adaptively incorporates historical control data and selectively adapt the treatment allocation ratios within an ongoing trial responsively to the relative treatment effects. The proposed design differs from existing designs that borrow from historical controls. As opposed to reducing the number of subjects assigned to the control arm blindly, this design does so adaptively to the relative treatment effects only if evaluation of cumulated current trial data combined with the historical control suggests the superiority of the intervention arm. We used the effective historical sample size approach to quantify borrowed information on the control arm and modified the treatment allocation rules of the doubly adaptive biased coin design to incorporate the quantity. The modified allocation rules were then implemented under the Bayesian framework with commensurate priors addressing prior-data conflict. Trials were also more frequently concluded earlier in line with the underlying truth, reducing trial cost, and duration and yielded parameter estimates with smaller standard errors.

An oncology clinical trial design with randomization adaptive to both short- and long-term responses

In oncology clinical trials, both short-term response and long-term survival are important. We propose an urn-based adaptive randomization design to incorporate both of these two outcomes. While short-term response can update the randomization probability quickly to benefit the trial participants, long-term survival outcome can also change the randomization to favor the treatment arm with definitive therapeutic benefit. Using generalized Friedman's urn, we derive an explicit formula for the limiting distribution of the number of subjects assigned to each arm. With prior or hypothetical knowledge on treatment effects, this formula can be used to guide the selection of parameters for the proposed design to achieve desirable patient number ratios between different treatment arms, and thus optimize the operating characteristics of the trial design. Simulation studies show that the proposed design successfully assign more patients to the treatment arms with either better short-term tumor response or long-term survival outcome or both.

Challenges and methodology in the incorporation of biomarkers in cancer clinical trials

Biomarkers can be used to establish more homogeneous groups using the genetic makeup of the tumour to inform the selection of treatment for each individual patient. However, proper preclinical work and stringent validation are needed before taking forward biomarkers into confirmatory studies. Despite the challenges, incorporation of biomarkers into clinical trials could better target appropriate patients, and potentially be lifesaving. The authors conducted a systematic review to describe marker-based and adaptive design methodology for their integration in clinical trials, and to further describe the associated practical challenges. Studies published between 1990 to November 2015 were searched on PubMed. Titles, abstracts and full text articles were reviewed to identify relevant studies. Of the 4438 studies examined, 57 studies were included. The authors conclude that the proposed approaches may readily help researchers to design biomarker trials, but novel approaches are still needed.

Statistical inference for response adaptive randomization procedures with adjusted optimal allocation proportions

Seamless phase II/III clinical trials have attracted increasing attention recently. They mainly use Bayesian response adaptive randomization (RAR) designs. There has been little research into seamless clinical trials using frequentist RAR designs because of the difficulty in performing valid statistical inference following this procedure. The well-designed frequentist RAR designs can target theoretically optimal allocation proportions, and they have explicit asymptotic results. In this paper, we study the asymptotic properties of frequentist RAR designs with adjusted target allocation proportions, and investigate statistical inference for this procedure. The properties of the proposed design provide an important theoretical foundation for advanced seamless clinical trials. Our numerical studies demonstrate that the design is ethical and efficient.

A novel evaluation of optimality for randomized controlled trials

Balanced two-arm designs are more powerful than unbalanced designs and, consequently, Bayesian adaptive designs (BADs) are less powerful. However, when considering other subject- or community-focused design characteristics, fixed two-arm designs can be suboptimal. We use a novel approach to identify the best two-arm study design, taking into consideration both the statistical perspective and the community's perception. Data envelopment analysis (DEA) was used to estimate the relative performance of competing designs in the presence of multiple optimality criteria. The two-arm fixed design has enough deficiencies in subject- and community-specific benefit to make it the least favorable study design.

Clinical trial designs incorporating predictive biomarkers

Development of oncologic therapies has traditionally been performed in a sequence of clinical trials intended to assess safety (phase I), preliminary efficacy (phase II), and improvement over the standard of care (phase III) in homogeneous (in terms of tumor type and disease stage) patient populations. As cancer has become increasingly understood on the molecular level, newer "targeted" drugs that inhibit specific cancer cell growth and survival mechanisms have increased the need for new clinical trial designs, wherein pertinent questions on the relationship between patient biomarkers and response to treatment can be answered. Herein, we review the clinical trial design literature from initial to more recently proposed designs for targeted agents or those treatments hypothesized to have enhanced effectiveness within patient subgroups (e.g., those with a certain biomarker value or who harbor a certain genetic tumor mutation). We also describe a number of real clinical trials where biomarker-based designs have been utilized, including a discussion of their respective advantages and challenges. As cancers become further categorized and/or reclassified according to individual patient and tumor features, we anticipate a continued need for novel trial designs to keep pace with the changing frontier of clinical cancer research.

Methods for identification and confirmation of targeted subgroups in clinical trials: A systematic review

Important objectives in the development of stratified medicines include the identification and confirmation of subgroups of patients with a beneficial treatment effect and a positive benefit-risk balance. We report the results of a literature review on methodological approaches to the design and analysis of clinical trials investigating a potential heterogeneity of treatment effects across subgroups. The identified approaches are classified based on certain characteristics of the proposed trial designs and analysis methods. We distinguish between exploratory and confirmatory subgroup analysis, frequentist, Bayesian and decision-theoretic approaches and, last, fixed-sample, group-sequential, and adaptive designs and illustrate the available trial designs and analysis strategies with published case studies.

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.

Outcome-adaptive randomization for a delayed outcome with a short-term predictor: imputation-based designs

Delay in the outcome variable is challenging for outcome-adaptive randomization, as it creates a lag between the number of subjects accrued and the information known at the time of the analysis. Motivated by a real-life pediatric ulcerative colitis trial, we consider a case where a short-term predictor is available for the delayed outcome. When a short-term predictor is not considered, studies have shown that the asymptotic properties of many outcome-adaptive randomization designs are little affected unless the lag is unreasonably large relative to the accrual process. These theoretical results assumed independent identical delays, however, whereas delays in the presence of a short-term predictor may only be conditionally homogeneous. We consider delayed outcomes as missing and propose mitigating the delay effect by imputing them. We apply this approach to the doubly adaptive biased coin design (DBCD) for motivating pediatric ulcerative colitis trial. We provide theoretical results that if the delays, although non-homogeneous, are reasonably short relative to the accrual process similarly as in the iid delay case, the lag is also asymptotically ignorable in the sense that a standard DBCD that utilizes only observed outcomes attains target allocation ratios in the limit. Empirical studies, however, indicate that imputation-based DBCDs performed more reliably in finite samples with smaller root mean square errors. The empirical studies assumed a common clinical setting where a delayed outcome is positively correlated with a short-term predictor similarly between treatment arm groups. We varied the strength of the correlation and considered fast and slow accrual settings.

Adaptive adjustment of the randomization ratio using historical control data

BACKGROUND

Prospective trial design often occurs in the presence of 'acceptable' historical control data. Typically, these data are only utilized for treatment comparison in a posteriori retrospective analysis to estimate population-averaged effects in a random-effects meta-analysis.

PURPOSE

We propose and investigate an adaptive trial design in the context of an actual randomized controlled colorectal cancer trial. This trial, originally reported by Goldberg et al., succeeded a similar trial reported by Saltz et al., and used a control therapy identical to that tested (and found beneficial) in the Saltz trial.

METHODS

The proposed trial implements an adaptive randomization procedure for allocating patients aimed at balancing total information (concurrent and historical) among the study arms. This is accomplished by assigning more patients to receive the novel therapy in the absence of strong evidence for heterogeneity among the concurrent and historical controls. Allocation probabilities adapt as a function of the effective historical sample size (EHSS), characterizing relative informativeness defined in the context of a piecewise exponential model for evaluating time to disease progression. Commensurate priors are utilized to assess historical and concurrent heterogeneity at interim analyses and to borrow strength from the historical data in the final analysis. The adaptive trial's frequentist properties are simulated using the actual patient-level historical control data from the Saltz trial and the actual enrollment dates for patients enrolled into the Goldberg trial.

RESULTS

Assessing concurrent and historical heterogeneity at interim analyses and balancing total information with the adaptive randomization procedure lead to trials that on average assign more new patients to the novel treatment when the historical controls are unbiased or slightly biased compared to the concurrent controls. Large magnitudes of bias lead to approximately equal allocation of patients among the treatment arms. Using the proposed commensurate prior model to borrow strength from the historical data, after balancing total information with the adaptive randomization procedure, provides admissible estimators of the novel treatment effect with desirable bias-variance trade-offs.

LIMITATIONS

Adaptive randomization methods in general are sensitive to population drift and more suitable for trials that initiate with gradual enrollment. Balancing information among study arms in time-to-event analyses is difficult in the presence of informative right-censoring.

CONCLUSIONS

The proposed design could prove important in trials that follow recent evaluations of a control therapy. Efficient use of the historical controls is especially important in contexts where reliance on preexisting information is unavoidable because the control therapy is exceptionally hazardous, expensive, or the disease is rare.

Evaluating many treatments and biomarkers in oncology: a new design

There is a pressing need for more-efficient trial designs for biomarker-stratified clinical trials. We suggest a new approach to trial design that links novel treatment evaluation with the concurrent evaluation of a biomarker within a confirmatory phase II/III trial setting. We describe a new protocol using this approach in advanced colorectal cancer called FOCUS4. The protocol will ultimately answer three research questions for a number of treatments and biomarkers: (1) After a period of first-line chemotherapy, do targeted novel therapies provide signals of activity in different biomarker-defined populations? (2) If so, do these definitively improve outcomes? (3) Is evidence of activity restricted to the biomarker-defined groups? The protocol randomizes novel agents against placebo concurrently across a number of different biomarker-defined population-enriched cohorts: BRAF mutation; activated AKT pathway: PI3K mutation/absolute PTEN loss tumors; KRAS and NRAS mutations; and wild type at all the mentioned genes. Within each biomarker-defined population, the trial uses a multistaged approach with flexibility to adapt in response to planned interim analyses for lack of activity. FOCUS4 is the first test of a protocol that assigns all patients with metastatic colorectal cancer to one of a number of parallel population-enriched, biomarker-stratified randomized trials. Using this approach allows questions regarding efficacy and safety of multiple novel therapies to be answered in a relatively quick and efficient manner, while also allowing for the assessment of biomarkers to help target treatment.

A two-stage patient enrichment adaptive design in phase II oncology trials

Illustrated is the use of a patient enrichment adaptive design in a randomized phase II trial which allows the evaluation of treatment benefits by the biomarker expression level and makes interim adjustment according to the pre-specified rules. The design was applied to an actual phase II metastatic hepatocellular carcinoma (HCC) trial in which progression-free survival (PFS) in two biomarker-defined populations is evaluated at both interim and final analyses. As an extension, a short-term biomarker is used to predict the long-term PFS in a Bayesian model in order to improve the precision of hazard ratio (HR) estimate at the interim analysis. The characteristics of the extended design are examined in a number of scenarios via simulations. The recommended adaptive design is shown to be useful in a phase II setting. When a short-term maker which correlates with the long-term PFS is available, the design can be applied in smaller early phase trials in which PFS requires longer follow-up. In summary, the adaptive design offers flexibility in randomized phase II patient enrichment trials and should be considered in an overall personalized healthcare (PHC) strategy.

Sequential design of phase II-III cancer trials

Although traditional phase II cancer trials are usually single arm, with tumor response as endpoint, and phase III trials are randomized and incorporate interim analyses with progression-free survival or other failure time as endpoint, this paper proposes a new approach that seamlessly expands a randomized phase II study of response rate into a randomized phase III study of time to failure. This approach is based on advances in group sequential designs and joint modeling of the response rate and time to event. The joint modeling is reflected in the primary and secondary objectives of the trial, and the sequential design allows the trial to adapt to increase in information on response and survival patterns during the course of the trial and to stop early either for conclusive evidence on efficacy of the experimental treatment or for the futility in continuing the trial to demonstrate it, on the basis of the data collected so far.

Innovative Clinical Trial Designs: Toward a 21st-Century Health Care System

Whereas the 20th-century health care system sometimes seemed to be inhospitable to and unmoved by experimental research, its inefficiency and unaffordability have led to reforms that foreshadow a new health care system. We point out certain opportunities and transformational needs for innovations in study design offered by the 21st-century health care system, and describe some innovative clinical trial designs and novel design methods to address these needs and challenges.

Bayesian adaptive clinical trials: a dream for statisticians only?

Adaptive or 'flexible' designs have emerged, mostly within frequentist frameworks, as an effective way to speed up the therapeutic evaluation process. Because of their flexibility, Bayesian methods have also been proposed for Phase I through Phase III adaptive trials; however, it has been reported that they are poorly used in practice. We aim to describe the international scientific production of Bayesian clinical trials by investigating the actual development and use of Bayesian 'adaptive' methods in the setting of clinical trials. A bibliometric study was conducted using the PubMed and Science Citation Index-Expanded databases. Most of the references found were biostatistical papers from various teams around the world. Most of the authors were from the US, and a large proportion was from the MD Anderson Cancer Center (University of Texas, Houston, TX). The spread and use of these articles depended heavily on their topic, with 3.1% of the biostatistical articles accumulating at least 25 citations within 5 years of their publication compared with 15% of the reviews and 32% of the clinical articles. We also examined the reasons for the limited use of Bayesian adaptive design methods in clinical trials and the areas of current and future research to address these challenges. Efforts to promote Bayesian approaches among statisticians and clinicians appear necessary.