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.

Inference in response-adaptive clinical trials when the enrolled population varies over time

A common assumption of data analysis in clinical trials is that the patient population, as well as treatment effects, do not vary during the course of the study. However, when trials enroll patients over several years, this hypothesis may be violated. Ignoring variations of the outcome distributions over time, under the control and experimental treatments, can lead to biased treatment effect estimates and poor control of false positive results. We propose and compare two procedures that account for possible variations of the outcome distributions over time, to correct treatment effect estimates, and to control type-I error rates. The first procedure models trends of patient outcomes with splines. The second leverages conditional inference principles, which have been introduced to analyze randomized trials when patient prognostic profiles are unbalanced across arms. These two procedures are applicable in response-adaptive clinical trials. We illustrate the consequences of trends in the outcome distributions in response-adaptive designs and in platform trials, and investigate the proposed methods in the analysis of a glioblastoma study.

Platform Trials to Expedite Drug Development in Alzheimer's Disease: A Report from the EU/US CTAD Task Force

A diverse range of platforms has been established to increase the efficiency and speed of clinical trials for Alzheimer's disease (AD). These platforms enable parallel assessment of multiple therapeutics, treatment regimens, or participant groups; use uniform protocols and outcome measures; and may allow treatment arms to be added or dropped based on interim analyses of outcomes. The EU/US CTAD Task Force discussed the lessons learned from the Dominantly Inherited Alzheimer's Network Trials Unit (DIAN-TU) platform trial and the challenges addressed by other platform trials that have launched or are in the planning stages. The landscape of clinical trial platforms in the AD space includes those testing experimental therapies such as DIAN-TU, platforms designed to test multidomain interventions, and those designed to streamline trial recruitment by building trial-ready cohorts. The heterogeneity of the AD patient population, AD drugs, treatment regimens, and analytical methods complicates the design and execution of platform trials, yet Task Force members concluded that platform trials are essential to advance the search for effective AD treatments, including combination therapies.

Treatment sequence network meta-analysis in Crohn's disease: a methodological case study

OBJECTIVE

Several biologic therapies are available for the treatment of mild-to-moderate Crohn's disease (CD). This network meta-analysis (NMA) aimed to assess the comparative efficacy of ustekinumab, adalimumab, vedolizumab and infliximab in the maintenance of clinical response and remission after 1 year of treatment.

METHODS

A systematic literature search was performed to identify relevant randomized controlled trials (RCTs). Key outcomes of interest were clinical response (CD activity index [CDAI] reduction of 100 points; CDAI-100) and remission (CDAI score under 150 points; CDAI < 150). A treatment sequence Bayesian NMA was conducted to account for the re-randomization of patients based on different clinical definitions, the lack of similarity of the common comparator for each trial and the full treatment pathway from the induction phase onwards.

RESULTS

Thirteen RCTs were identified. Ustekinumab 90 mg q8w was associated with statistically significant improvement in clinical response relative to placebo and vedolizumab 300 mg. For clinical remission, ustekinumab 90 mg q8w was associated with statistically significant improvement relative to placebo and vedolizumab 300 mg q8w. Findings from sub-population analyses had similar results but were not statistically significant.

CONCLUSIONS

The NMA suggest that ustekinumab is associated with the highest likelihood of reaching response or remission at 1 year compared with placebo, adalimumab and vedolizumab. Results should be interpreted with caution because this is a novel methodology; however, the treatment sequence analysis may be the most methodologically sound analysis to derive estimates of comparative efficacy in CD in the absence of head-to-head evidence.

The Performance of Fixed-Horizon, Look-Ahead Procedures Compared to Backward Induction in Bayesian Adaptive-Randomization Decision-Theoretic Clinical Trial Design

Designing optimal, Bayesian decision-theoretic trials has traditionally required the use of computationally-intensive backward induction. While methods for addressing this barrier have been put forward, few are both computationally tractable and non-myopic, with applications of the Gittins index being one notable example. Here we explore the look-ahead approach with adaptive-randomization, with designs ranging from the fully myopic to the fully informed. We compare the operating characteristics of the look-ahead designed trials, in which decision rules are based on a fixed number of future blocks, with those of trials designed using traditional backward induction. The less-myopic designs performed well. As the designs become more myopic or the trials longer, there were disparities in regions of the decision space that are transition zones between continuation and stopping decisions. The more myopic trials generally suffered from early stopping as compared to the less myopic and backward induction trials. Myopic trials with adaptive randomization also saw as many as 28 % of their continuation decisions change to a different randomization ratio as compared to the backward induction designs. Finally, early stages of myopic-designed trials may have disproportionate effect on trial characteristics.

Highlighting the need for reliable clinical trials in glioblastoma

INTRODUCTION

Several recent phase III studies have attempted to improve the dismal survival seen in glioblastoma patients, with disappointing results despite prior promising phase II data. Areas covered: A literature review of prior phase II and phase III studied in glioblastoma was performed to help identify possible areas of concern. Numerous issues in previous phase II trials for glioblastoma were found that may have contributed to these discouraging outcomes and discordant results. Expert commentary: These concerns include the improper selection of therapeutics warranting investigation in a phase III trial, suboptimal design of phase II studies (often lacking a control arm), absence of molecular data, the use of imaging criteria as a surrogate endpoint, and a lack of pharmacodynamic testing. Hopefully, by recognizing prior phase II trial limitations that contributed to failed phase III trials, we can adapt quickly to improve our ability to accurately discover survival-prolonging treatments for glioblastoma patients.

Opportunities and Challenges for Drug Development: Public-Private Partnerships, Adaptive Designs and Big Data

Drug development faces the double challenge of increasing costs and increasing pressure on pricing. To avoid that lack of perceived commercial perspective will leave existing medical needs unmet, pharmaceutical companies and many other stakeholders are discussing ways to improve the efficiency of drug Research and Development. Based on an international symposium organized by the Medical School of the University of Duisburg-Essen (Germany) and held in January 2016, we discuss the opportunities and challenges of three specific areas, i.e., public–private partnerships, adaptive designs and big data. Public–private partnerships come in many different forms with regard to scope, duration and type and number of participants. They range from project-specific collaborations to strategic alliances to large multi-party consortia. Each of them offers specific opportunities and faces distinct challenges. Among types of collaboration, investigator-initiated studies are becoming increasingly popular but have legal, ethical, and financial implications. Adaptive trial designs are also increasingly discussed. However, adaptive should not be used as euphemism for the repurposing of a failed trial; rather it requires carefully planning and specification before a trial starts. Adaptive licensing can be a counter-part of adaptive trial design. The use of Big Data is another opportunity to leverage existing information into knowledge useable for drug discovery and development. Respecting limitations of informed consent and privacy is a key challenge in the use of Big Data. Speakers and participants at the symposium were convinced that appropriate use of the above new options may indeed help to increase the efficiency of future drug development.

Power of an Adaptive Trial Design for Endovascular Stroke Studies: Simulations Using IMS (Interventional Management of Stroke) III Data

BACKGROUND AND PURPOSE

Adaptive trial designs that allow enrichment of the study population through subgroup selection can increase the chance of a positive trial when there is a differential treatment effect among patient subgroups. The goal of this study is to illustrate the potential benefit of adaptive subgroup selection in endovascular stroke studies.

METHODS

We simulated the performance of a trial design with adaptive subgroup selection and compared it with that of a traditional design. Outcome data were based on 90-day modified Rankin Scale scores, observed in IMS III (Interventional Management of Stroke III), among patients with a vessel occlusion on baseline computed tomographic angiography (n=382). Patients were categorized based on 2 methods: (1) according to location of the arterial occlusive lesion and onset-to-randomization time and (2) according to onset-to-randomization time alone. The power to demonstrate a treatment benefit was based on 10 000 trial simulations for each design.

RESULTS

The treatment effect was relatively homogeneous across categories when patients were categorized based on arterial occlusive lesion and time. Consequently, the adaptive design had similar power (47%) compared with the fixed trial design (45%). There was a differential treatment effect when patients were categorized based on time alone, resulting in greater power with the adaptive design (82%) than with the fixed design (57%).

CONCLUSIONS

These simulations, based on real-world patient data, indicate that adaptive subgroup selection has merit in endovascular stroke trials as it substantially increases power when the treatment effect differs among subgroups in a predicted pattern.

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 National Lung Matrix Trial: translating the biology of stratification in advanced non-small-cell lung cancer

BACKGROUND

The management of NSCLC has been transformed by stratified medicine. The National Lung Matrix Trial (NLMT) is a UK-wide study exploring the activity of rationally selected biomarker/targeted therapy combinations.

PATIENTS AND METHODS

The Cancer Research UK (CRUK) Stratified Medicine Programme 2 is undertaking the large volume national molecular pre-screening which integrates with the NLMT. At study initiation, there are eight drugs being used to target 18 molecular cohorts. The aim is to determine whether there is sufficient signal of activity in any drug-biomarker combination to warrant further investigation. A Bayesian adaptive design that gives a more realistic approach to decision making and flexibility to make conclusions without fixing the sample size was chosen. The screening platform is an adaptable 28-gene Nextera next-generation sequencing platform designed by Illumina, covering the range of molecular abnormalities being targeted. The adaptive design allows new biomarker-drug combination cohorts to be incorporated by substantial amendment. The pre-clinical justification for each biomarker-drug combination has been rigorously assessed creating molecular exclusion rules and a trumping strategy in patients harbouring concomitant actionable genetic abnormalities. Discrete routes of pathway activation or inactivation determined by cancer genome aberrations are treated as separate cohorts. Key translational analyses include the deep genomic analysis of pre- and post-treatment biopsies, the establishment of patient-derived xenograft models and longitudinal ctDNA collection, in order to define predictive biomarkers, mechanisms of resistance and early markers of response and relapse.

CONCLUSION

The SMP2 platform will provide large scale genetic screening to inform entry into the NLMT, a trial explicitly aimed at discovering novel actionable cohorts in NSCLC.

CLINICAL TRIAL ISRCTN

38344105.

Selecting the right tool for the job

There are competing ethical concerns when it comes to designing any clinical research study. Clinical trials of possible treatments for Ebola virus are no exception. If anything, the competing ethical concerns are exacerbated in trying to find answers to a deadly, rapidly spreading, infectious disease. The primary goal of current research is to identify experimental therapies that can cure Ebola or cure it with reasonable probability in infected individuals. Pursuit of that goal must be methodologically sound, practical and consistent with prevailing norms governing human subjects research. Some maintain that only randomized controlled trials (RCTs) with a placebo or standard-of-care arm can meet these conditions. We maintain that there are alternative trial designs that can do so as well and that sometimes these are preferable to RCTs.

Adaptive Clinical Trials: Overview of Phase III Designs and Challenges

Adaptive designs use accruing data to make changes in an ongoing trial according to a prespecified plan and potentially offer great efficiencies for clinical development. There are many types of adaptive designs and many trial aspects that could in theory be adapted. However, the scope of adaptive designs with relevance in confirmatory trials is narrower, and in addition, extensive pre-planning is needed and various types of challenges need to be addressed in order to use these designs in this stage of development. Nevertheless, with careful planning, there are opportunities for these designs to offer important benefits even in the confirmatory stage of development. We provide an overview of adaptive designs that have relevance for confirmatory trials and discuss considerations that may affect whether they should or should not be used in particular trials or programs as well as the challenges that need to be addressed.

Response-adaptive decision-theoretic trial design: operating characteristics and ethics

Adaptive randomization is used in clinical trials to increase statistical efficiency. In addition, some clinicians and researchers believe that using adaptive randomization leads necessarily to more ethical treatment of subjects in a trial. We develop Bayesian, decision-theoretic, clinical trial designs with response-adaptive randomization and a primary goal of estimating treatment effect and then contrast these designs with designs that also include in their loss function a cost for poor subject outcome. When the loss function did not incorporate a cost for poor subject outcome, the gains in efficiency from response-adaptive randomization were accompanied by ethically concerning subject allocations. Conversely, including a cost for poor subject outcome demonstrated a more acceptable balance between the competing needs in the trial. A subsequent, parallel set of trials designed to control explicitly types I and II error rates showed that much of the improvement achieved through modification of the loss function was essentially negated. Therefore, gains in efficiency from the use of a decision-theoretic, response-adaptive design using adaptive randomization may only be assumed to apply to those goals that are explicitly included in the loss function. Trial goals, including ethical ones, which do not appear in the loss function, are ignored and may even be compromised; it is thus inappropriate to assume that all adaptive trials are necessarily more ethical. Controlling types I and II error rates largely negates the benefit of including competing needs in favor of the goal of parameter estimation.

Bayesian clinical trials in action

Although the frequentist paradigm has been the predominant approach to clinical trial design since the 1940s, it has several notable limitations. Advancements in computational algorithms and computer hardware have greatly enhanced the alternative Bayesian paradigm. Compared with its frequentist counterpart, the Bayesian framework has several unique advantages, and its incorporation into clinical trial design is occurring more frequently. Using an extensive literature review to assess how Bayesian methods are used in clinical trials, we find them most commonly used for dose finding, efficacy monitoring, toxicity monitoring, diagnosis/decision making, and studying pharmacokinetics/pharmacodynamics. The additional infrastructure required for implementing Bayesian methods in clinical trials may include specialized software programs to run the study design, simulation and analysis, and web-based applications, all of which are particularly useful for timely data entry and analysis. Trial success requires not only the development of proper tools but also timely and accurate execution of data entry, quality control, adaptive randomization, and Bayesian computation. The relative merit of the Bayesian and frequentist approaches continues to be the subject of debate in statistics. However, more evidence can be found showing the convergence of the two camps, at least at the practical level. Ultimately, better clinical trial methods lead to more efficient designs, lower sample sizes, more accurate conclusions, and better outcomes for patients enrolled in the trials. Bayesian methods offer attractive alternatives for better trials. More Bayesian trials should be designed and conducted to refine the approach and demonstrate their real benefit in action.