Use of Seamless Study Designs in Oncology Clinical Development- A Survey Conducted by IDSWG Oncology Sub-team

Seamless study designs have the potential to accelerate clinical development. The use of innovative seamless designs has been increasing in the oncology area; however, while the concept of seamless designs becomes more popular and accepted, many challenges remain in both the design and conduct of these trials. This may be especially true when seamless designs are used in late phase development supporting regulatory decision-making. The Innovative Design Scientific Working Group (IDSWG) Oncology team conducted a survey to understand the current use of seamless study designs for registration purposes in oncology clinical development. The survey was designed to provide insights into the benefits and to identify the roadblocks. A total of 16 questions were included in the survey that was distributed using the ASA Biopharmaceutical Section and IDSWG email listings from August to September 2022. A total of 51 responses were received, with 39 (76%) respondents indicating that their organizations had seamless oncology studies in planning or implementation for registration purposes. Detailed survey results are presented in the manuscript. Overall, while seamless designs offer advantages in terms of timeline reduction and cost saving, they also present challenges related to additional complexity and the need for efficient surrogate clinical endpoints in oncology drug development.

Rare disease clinical trials in the European Union: navigating regulatory and clinical challenges

BACKGROUND

Clinical development for orphan drugs presents significant difficulties and challenges. There is no unique or standard design, conduct, and outcome assessment methodology and it is sometimes impractical to fit design models of rare disease trials in any practiced and well-known framework. In the European Union (EU) these challenges encompass a broad array of subjects, including trial design, study outcomes, patient recruitment, trial conduct ethics, trial cost, and chances of success. This literature-based review study aims to provide a thorough overview of the critical aspects of rare disease trials in the EU by analyzing the current landscape of rare disease trials, highlighting key challenges, delving into regulatory and research initiatives and innovation in trial designs, and proposing multi-faceted solutions to implement effective rare disease clinical trials in the region.

DISCUSSION

Traditional clinical trial designs, validation, and evaluation methodologies used for nonorphan drugs often prove unsuitable for orphan drugs, given the small patient populations, sometimes fewer than 1000 cases. There is an increasing need for accessible therapies and both regulators as well as industry are trying to develop affordable and effective drugs to address this need. Despite several steps that have been taken, the timely development of drugs remains a challenge. One of the reasons behind the long development timeline is the recruitment, retention, and conduct of rare disease trials. To optimize the development timelines of orphan drugs in the EU, it is important to ensure that the safety and efficacy of the product is not compromised. Industry and regulatory agencies must implement innovative trial designs, devise flexible policies, and incorporate real-world data for assessing clinical outcomes.

CONCLUSION

Collaboration among academic institutions, pharmaceutical companies (both small and major), patient groups, and health authorities is crucial in overcoming obstacles related to clinical trials and providing assistance and creative ideas. The ultimate objective of granting rare disease patients timely and affordable access to medications with a positive balance between benefits and risks is to be met.

Optimal sample size division in two-stage seamless designs

Inferentially seamless 2/3 designs are increasingly popular in clinical trials. It is important to understand their relative advantages compared with separate phase 2 and phase 3 trials, and to understand the consequences of design choices such as the proportion of patients included in the phase 2 portion of the design. Extending previous work in this area, we perform a simulation study across multiple numbers of arms and efficacy response curves. We consider a design space crossing the choice of a separate versus seamless design with the choice of allocating 0%-100% of available patients in phase 2, with the remainder in phase 3. The seamless designs achieve greater power than their separate trial counterparts. Importantly, the optimal seamless design is more robust than the optimal separate program, meaning that one range of values for the proportion of patients used in phase 2 (30%-50% of the total phase 2/3 sample size) is nearly optimal for a wide range of response scenarios. In contrast, a percentage of patients used in phase 2 for separate trials may be optimal for some alternative scenarios but decidedly inferior for other alternative scenarios. When operationally and scientifically viable, seamless trials provide superior performance compared with separate phase 2 and phase 3 trials. The results also provide guidance for the implementation of these trials in practice.

A diagnostic phase III/IV seamless design to investigate the diagnostic accuracy and clinical effectiveness using the example of HEDOS and HEDOS II

The development process of medical devices can be streamlined by combining different study phases. Here, for a diagnostic medical device, we present the combination of confirmation of diagnostic accuracy (phase III) and evaluation of clinical effectiveness regarding patient-relevant endpoints (phase IV) using a seamless design. This approach is used in the Thyroid HEmorrhage DetectOr Study (HEDOS & HEDOS II) investigating a post-operative hemorrhage detector named ISAR-M THYRO® in patients after thyroid surgery. Data from the phase III trial are reused as external controls in the control group of the phase IV trial. An unblinded interim analysis is planned between the two study stages which includes a recalculation of the sample size for the phase IV part after completion of the first stage of the seamless design. The study concept presented here is the first seamless design proposed in the field of diagnostic studies. Hence, the aim of this work is to emphasize the statistical methodology as well as feasibility of the proposed design in relation to the planning and implementation of the seamless design. Seamless designs can accelerate the overall trial duration and increase its efficiency in terms of sample size and recruitment. However, careful planning addressing numerous methodological and procedural challenges is necessary for successful implementation as well as agreement with regulatory bodies.

Seamless phase 2/3 design for trials with multiple co-primary endpoints using Bayesian predictive power

Seamless phase 2/3 design has become increasingly popular in clinical trials with a single endpoint. Trials that define success based on the achievement of all co-primary endpoints (CPEs) encounter the challenge of inflated type 2 error rates, often leading to an overly large sample size. To tackle this challenge, we introduced a seamless phase 2/3 design strategy that employs Bayesian predictive power (BPP) for futility monitoring and sample size re-estimation at interim analysis. The correlations among multiple CPEs are incorporated using a Dirichlet-multinomial distribution. An alternative approach based on conditional power (CP) was also discussed for comparison. A seamless phase 2/3 vaccine trial employing four binary endpoints under the non-inferior hypothesis serves as an example. Our results spotlight that, in scenarios with relatively small phase 2 sample sizes (e.g., 50 or 100 subjects), the BPP approach either outperforms or matches the CP approach in terms of overall power. Particularly, with n1 = 50 and ρ = 0, BPP showcases an overall power advantage over CP by as much as 8.54%. Furthermore, when the phase 2 stage enrolled more subjects (e.g., 150 or 200), especially with a phase 2 sample size of 200 and ρ = 0, the BPP approach evidences a peak difference of 5.76% in early stop probability over the CP approach, emphasizing its better efficiency in terminating futile trials. It's noteworthy that both BPP and CP methodologies maintained type 1 error rates under 2.5%. In conclusion, the integration of the Dirichlet-Multinominal model with the BPP approach offers improvement in certain scenarios over the CP approach for seamless phase 2/3 trials with multiple CPEs.

Using dichotomized survival data to construct a prior distribution for a Bayesian seamless Phase II/III clinical trial

Master protocol designs allow for simultaneous comparison of multiple treatments or disease subgroups. Master protocols can also be designed as seamless studies, in which two or more clinical phases are considered within the same trial. They can be divided into two categories: operationally seamless, in which the two phases are separated into two independent studies, and inferentially seamless, in which the interim analysis is considered an adaptation of the study. Bayesian designs are scarcely studied. Our aim is to propose and compare Bayesian operationally seamless Phase II/III designs using a binary endpoint for the first stage and a time-to-event endpoint for the second stage. At the end of Phase II, arm selection is based on posterior (futility) and predictive (selection) probabilities. The results of the first phase are then incorporated into prior distributions of a time-to-event model. Simulation studies showed that Bayesian operationally seamless designs can approach the inferentially seamless counterpart, allowing for an increasing simulated power with respect to the operationally frequentist design.

Recent innovations in adaptive trial designs: A review of design opportunities in translational research

Clinical trials are constantly evolving in the context of increasingly complex research questions and potentially limited resources. In this review article, we discuss the emergence of "adaptive" clinical trials that allow for the preplanned modification of an ongoing clinical trial based on the accumulating evidence with application across translational research. These modifications may include terminating a trial before completion due to futility or efficacy, re-estimating the needed sample size to ensure adequate power, enriching the target population enrolled in the study, selecting across multiple treatment arms, revising allocation ratios used for randomization, or selecting the most appropriate endpoint. Emerging topics related to borrowing information from historic or supplemental data sources, sequential multiple assignment randomized trials (SMART), master protocol and seamless designs, and phase I dose-finding studies are also presented. Each design element includes a brief overview with an accompanying case study to illustrate the design method in practice. We close with brief discussions relating to the statistical considerations for these contemporary designs.

Lung Cancer Clinical Trials with a Seamless Phase II/III Design: Systematic Review

Current lung cancer clinical research focuses on biomarkers and personalized treatment strategies. Adaptive clinical trial designs have gained significant ground due to their increased flexibility, compared to the conventional model of drug development from phase I to phase IV trials. One such adaptive approach is the seamless phase II/III design, which has been used to reduce the total sample size and drug development time. In this context, an algorithmic systematic search was conducted in MEDLINE (PUBMED), SCOPUS, EMBASE and Cochrane Central Register of Controlled Trials until 31 June 2022 in order to identify lung cancer trials of systematic treatments that have employed the seamless phase II/III methodology and to describe their characteristics. The search strategy yielded a total of 1420 records that were screened through their title and abstract; 28 eligible trials were included in the systematic review. Based on the study endpoints, the most common subtype included phase II/III trials with inefficacy/futility analyses (61%; 17/28), followed by dose escalation phase II/III trials (18%; 5/28), one multi-arm multi stage trial and 5 trials with other design (18%). Most eligible trials were open-label (71%; 20/27), included patients with non-small cell lung cancer (82%; 23/28), evaluated targeted therapies and/or immunotherapies (82%; 23/28) and recruited patients with advanced disease (89.3%; 25/28). In conclusion, the seamless phase II/III design is a feasible and suitable approach in lung cancer research, with distinct design subcategories according to study endpoints.

Statistical tests for two-stage adaptive seamless design using short- and long-term binary outcomes

The adaptive seamless design combining phases II and III into a single trial has been shown growing interest for improving the efficiency of drug development, becoming the most frequent adaptive design type. It typically consists of two stages, the trial objectives being often different in each stage. The primary objectives are to select optimal experimental treatment group(s) in the first stage and compare the efficacy between the selected treatment and control groups in the second stage. In this article, we focus on a two-stage adaptive seamless design, for which treatment selection is based on the short-term binary endpoint and treatment comparison is based on the long-term binary endpoint. We thus propose an exact conditional test as a final analysis, based on the bivariate binomial distribution and given the selected treatment with the most promising short-term endpoint response rate from an interim analysis. Additionally, the mid- p $$ p $$ approach is incorporated to improve conservativeness for an exact test. Simulation studies were conducted to compare the proposed methods with a method based on the combination test. The proposed exact method controlled for type I error rate at the nominal level, regardless of the number of initial treatments or the correlation between short- and long-term endpoints. In terms of the treatment comparison power, the proposed methods are more powerful than that based on the combination test in the scenarios, with only one treatment being effective.

Application of machine learning methods in clinical trials for precision medicine

OBJECTIVE

A key component for precision medicine is a good prediction algorithm for patients' response to treatments. We aim to implement machine learning (ML) algorithms into the response-adaptive randomization (RAR) design and improve the treatment outcomes.

MATERIALS AND METHODS

We incorporated 9 ML algorithms to model the relationship of patient responses and biomarkers in clinical trial design. Such a model predicted the response rate of each treatment for each new patient and provide guidance for treatment assignment. Realizing that no single method may fit all trials well, we also built an ensemble of these 9 methods. We evaluated their performance through quantifying the benefits for trial participants, such as the overall response rate and the percentage of patients who receive their optimal treatments.

RESULTS

Simulation studies showed that the adoption of ML methods resulted in more personalized optimal treatment assignments and higher overall response rates among trial participants. Compared with each individual ML method, the ensemble approach achieved the highest response rate and assigned the largest percentage of patients to their optimal treatments. For the real-world study, we successfully showed the potential improvements if the proposed design had been implemented in the study.

CONCLUSION

In summary, the ML-based RAR design is a promising approach for assigning more patients to their personalized effective treatments, which makes the clinical trial more ethical and appealing. These features are especially desirable for late-stage cancer patients who have failed all the Food and Drug Administration (FDA)-approved treatment options and only can get new treatments through clinical trials.

An Overview of Phase 2 Clinical Trial Designs

Clinical trials are studies to test new treatments in humans. Typically, these treatments are evaluated over several phases to assess their safety and efficacy. Phase 1 trials are designed to evaluate the safety and tolerability of a new treatment, typically with a small number of patients (eg, 20-80), generally spread across several dose levels. Phase 2 trials are designed to determine whether the new treatment has sufficiently promising efficacy to warrant further investigation in a large-scale randomized phase 3 trial, as well as to further assess safety. These studies usually involve a few hundred patients. This article provides an overview of some of the most commonly used phase 2 designs for clinical trials and emphasizes their critical elements and considerations. Key references to some of the most commonly used phase 2 designs are given to allow the reader to explore in more detail the critical aspects when planning a phase 2 trial. A comparison of 3 potential designs in the context of the NRG-HN002 trial is presented to complement the discussion about phase 2 trials.

Classification of Companion Diagnostics: A New Framework for Biomarker-Driven Patient Selection

Background

Modern personalized medicine strategies builds on therapy companion diagnostics to stratify patients into subgroups with differential benefit/risk. In general, stratification for drug response implies a treatment-by-subgroup interaction. This interaction is usually suggested by the drug’s mechanism of action and investigated in pharmacological research or in clinical studies. In these candidate genes or pathway approaches, either biological reasons for a differential benefit/risk or statistical interaction regarding a pharmacological or clinical endpoint or both may be given. For successful drug approval, demonstration of a positive benefit/risk balance in the intended patient population is required. This also applies to situations with biomarker-selected populations. However, further regulatory considerations relate to the usefulness and plausibility of the selected patients and benefit/risk extrapolations or alternative therapy options in biomarker-negative populations.

Methods

To facilitate the specification of regulatory requirements and support the design of clinical development programmes, a systematic classification of biomarker-drug pairs is needed, in particular with regard to the expected underlying molecular mechanism and the clinical evidence.

Results

A classification of five biomarker-drug categories is proposed related to increasing evidence on the biomarker’s predictive value in relation to a specific drug. We classified biomarkers into five ascending categories with increasing evidence on the predictive nature of the biomarker in relation to a specific drug according to the comparative pharmacological and clinical evidence.

Conclusions

The proposed classification will facilitate regulatory decision-making and support drug development with respect to biomarker-related subgrouping, both, during clinical programme and at the time of marketing authorization application, since the grade of evidence on the differential power of the biomarker can be considered as an indicator for the usefulness of a biomarker-related subgrouping.

Opportunity for efficiency in clinical development: An overview of adaptive clinical trial designs and innovative machine learning tools, with examples from the cardiovascular field

Modern data analysis tools and statistical modeling techniques are increasingly used in clinical research to improve diagnosis, estimate disease progression and predict treatment outcomes. What seems less emphasized is the importance of the study design, which can have a serious impact on the study cost, time and statistical efficiency. This paper provides an overview of different types of adaptive designs in clinical trials and their applications to cardiovascular trials. We highlight recent proliferation of work on adaptive designs over the past two decades, including some recent regulatory guidelines on complex trial designs and master protocols. We also describe the increasing role of machine learning and use of metaheuristics to construct increasingly complex adaptive designs or to identify interesting features for improved predictions and classifications.

An adaptive seamless Phase 2-3 design with multiple endpoints

Adaptive seamless Phase 2-3 design has been considered as one possible way to expedite development time for a drug program by allowing the expansion from an ongoing Phase 2 trial into a Phase 3 trial. Multiple endpoints are often tested when a regulatory approval is pursued. Here we propose an adaptive seamless Phase 2-3 design with multiple endpoints which can expand an ongoing Phase 2 trial into a Phase 3 trial based on an intermediate endpoint for adaptive decision and test the endpoints with a powerful multiple test procedure. It is proved that the proposed design can preserve the familywise Type I error under a mild assumption that is expected to hold in practical considerations. We illustrate our proposed design with an example trial design for oncology. Simulations are conducted to confirm the control of the familywise Type I error and the adaptive seamless Phase 2-3 design is illustrated with an example.

Optimizing Sample Size Allocation and Power in a Bayesian Two-Stage Drop-The-Losers Design

When a researcher desires to test several treatment arms against a control arm, a two-stage adaptive design can be more efficient than a single-stage design where patients are equally allocated to all treatment arms and the control. We see this type of approach in clinical trials as a seamless Phase II - Phase III design. These designs require more statistical support and are less straightforward to plan and analyze than a standard single-stage design. To diminish the barriers associated with a Bayesian two-stage drop-the-losers design, we built a user-friendly point-and-click graphical user interface with R Shiny to aid researchers in planning such designs by allowing them to easily obtain trial operating characteristics, estimate statistical power and sample size, and optimize patient allocation in each stage to maximize power. We assume that endpoints are distributed normally with unknown but common variance between treatments. We recommend this software as an easy way to engage statisticians and researchers in two-stage designs as well as to actively investigate the power of two-stage designs relative to more traditional approaches. The software is freely available at https://github.com/stefangraw/Allocation-Power-Optimizer.

A proof-of-concept-to-confirmatory multiple adaptation design in the development of an anti-viral treatment

In the clinical development of some new infectious disease drugs, early clinical pharmacology trials may predict with high confidence that the efficacious doses are well below the range of the safety margin. In this case, a dose-ranging study may be unnecessary after a proof-of-concept (PoC) study testing the highest dose. A multi-stage adaptive design spanning both PoC and confirmatory stages is proposed in this context. The design incorporates two interim analyses allowing strategies for stopping, continuing, or expanding the study. A conditional power threshold for a binary endpoint is proposed to assess futility. Additional components of early efficacy and sample size adjustment are also included to enhance the design's flexibility and robustness. Design operating characteristics are evaluated by numerical calculation. We show that the proposed streamlined trial design has the same statistical rigor as a conventional phase 3 clinical trial with adequate power and a properly controlled type 1 error rate. Additional adaptive design options are also investigated and discussed.

Adaptive designs in clinical trials: why use them, and how to run and report them

Adaptive designs can make clinical trials more flexible by utilising results accumulating in the trial to modify the trial's course in accordance with pre-specified rules. Trials with an adaptive design are often more efficient, informative and ethical than trials with a traditional fixed design since they often make better use of resources such as time and money, and might require fewer participants. Adaptive designs can be applied across all phases of clinical research, from early-phase dose escalation to confirmatory trials. The pace of the uptake of adaptive designs in clinical research, however, has remained well behind that of the statistical literature introducing new methods and highlighting their potential advantages. We speculate that one factor contributing to this is that the full range of adaptations available to trial designs, as well as their goals, advantages and limitations, remains unfamiliar to many parts of the clinical community. Additionally, the term adaptive design has been misleadingly used as an all-encompassing label to refer to certain methods that could be deemed controversial or that have been inadequately implemented.We believe that even if the planning and analysis of a trial is undertaken by an expert statistician, it is essential that the investigators understand the implications of using an adaptive design, for example, what the practical challenges are, what can (and cannot) be inferred from the results of such a trial, and how to report and communicate the results. This tutorial paper provides guidance on key aspects of adaptive designs that are relevant to clinical triallists. We explain the basic rationale behind adaptive designs, clarify ambiguous terminology and summarise the utility and pitfalls of adaptive designs. We discuss practical aspects around funding, ethical approval, treatment supply and communication with stakeholders and trial participants. Our focus, however, is on the interpretation and reporting of results from adaptive design trials, which we consider vital for anyone involved in medical research. We emphasise the general principles of transparency and reproducibility and suggest how best to put them into practice.

Addressing Challenges and Opportunities of "Less Well-Understood" Adaptive Designs

The draft adaptive design guidance released by FDA in 2010 included references to adaptive study designs that were described as "less well-understood." At that time, there was relatively little regulatory experience with such designs, and their properties were felt to be insufficiently understood. In order to promote greater use of adaptive designs, especially those categorized as less well-understood, the Best Practice Subteam of the DIA Adaptive Designs Scientific Working Group (ADSWG) has worked on describing and characterizing these designs, identifying challenges associated with them and suggesting improvements to design or study conduct aspects that might make them more acceptable. This paper summarizes the work from the subteam.

Adaptive Clinical Trial Design: An Overview and Potential Applications in Dermatology

The challenges of drug development, including increasing costs, late-stage drug failures, and the decline in the number of drugs being approved by the US Food and Drug Administration over time, have generated interest in adaptive study designs that have the potential to address these problems. Adaptive trial designs use interim data analysis to amend trials, and have been recognized for more than a decade as a way to increase trial efficiency, partly by the increased probability of demonstrating a drug effect if one exists. In this article, we define adaptive trials; give examples of the most common types; highlight the pros, cons, and ethical considerations of these designs; and illustrate how these tools can be applied to drug development in dermatology.

Twenty-five years of confirmatory adaptive designs: opportunities and pitfalls

'Multistage testing with adaptive designs' was the title of an article by Peter Bauer that appeared 1989 in the German journal Biometrie und Informatik in Medizin und Biologie. The journal does not exist anymore but the methodology found widespread interest in the scientific community over the past 25 years. The use of such multistage adaptive designs raised many controversial discussions from the beginning on, especially after the publication by Bauer and Köhne 1994 in Biometrics: Broad enthusiasm about potential applications of such designs faced critical positions regarding their statistical efficiency. Despite, or possibly because of, this controversy, the methodology and its areas of applications grew steadily over the years, with significant contributions from statisticians working in academia, industry and agencies around the world. In the meantime, such type of adaptive designs have become the subject of two major regulatory guidance documents in the US and Europe and the field is still evolving. Developments are particularly noteworthy in the most important applications of adaptive designs, including sample size reassessment, treatment selection procedures, and population enrichment designs. In this article, we summarize the developments over the past 25 years from different perspectives. We provide a historical overview of the early days, review the key methodological concepts and summarize regulatory and industry perspectives on such designs. Then, we illustrate the application of adaptive designs with three case studies, including unblinded sample size reassessment, adaptive treatment selection, and adaptive endpoint selection. We also discuss the availability of software for evaluating and performing such designs. We conclude with a critical review of how expectations from the beginning were fulfilled, and - if not - discuss potential reasons why this did not happen.

Changing the Paradigm for the Treatment and Development of New Therapies for FSGS

Focal segmental glomerulosclerosis (FSGS) is a renal pathology finding that represents a constellation of rare kidney diseases, which manifest as proteinuria, edema nephrotic syndrome, hypertension, and increased risk for kidney failure. Therapeutic options for FSGS are reviewed displaying the expected efficacy from 25 to 69% depending on specific therapy, patient characteristics, cost, and common side effects. This variability in treatment response is likely caused, in part, by the heterogeneity in the etiology and active molecular mechanisms of FSGS. Clinical trials in FSGS have been scant in number and slow to recruit, which may stem, in part, from reliance on classic clinical trial design paradigms. Traditional clinical trial designs based on the "learn and confirm" paradigm may not be appropriate for rare diseases, such as FSGS. Future drug development and testing will require novel approaches to trial designs that have the capacity to enrich study populations and adapt the trial in a planned way to gain efficiencies in trial completion timelines. A clinical trial simulation is provided that compares a classical and more modern design to determine the maximum tolerated dose in FSGS.

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.

A Practical Guide to Data Monitoring Committees in Adaptive Trials

Adaptive clinical trials require access to interim data to carry out trial modification as allowed by a prespecified adaptation plan. A data monitoring committee (DMC) is a group of experts that is charged with monitoring accruing trial data to ensure the safety of trial participants and that in adaptive trials may also play a role in implementing a preplanned adaptation. In this paper, we summarize current practices and viewpoints and provide guidance on evolving issues related to the use of DMCs in adaptive trials. We describe the common types of adaptive designs and point out some DMC-related issues that are unique to this class of designs. We include 3 examples of DMCs in late-stage adaptive trials that have been implemented in practice. We advocate training opportunities for researchers who may be interested in serving on a DMC for an adaptive trial since qualified DMC members are fundamental to the successful execution of DMC responsibilities.

The Add-Arm Design for Unimodal Response Curve with Unknown Mode

In a classical drop-loser (or drop-arm) design, patients are randomized into all arms (doses) and at the interim analysis, inferior arms are dropped. Therefore, compared to the traditional dose-finding design, this adaptive design can reduce the sample size by not carrying over all doses to the end of the trial or dropping the losers earlier. However, all the doses have to be explored. For unimodal (including linear or umbrella) response curves, we proposed an effective dose-finding design that allows adding arms at the interim analysis. The trial design starts with two arms, depending on the response of the two arms and the unimodality assumption; we can decide which new arms to be added. This design does not require exploring all arms (doses) to find the best responsive dose; therefore, it can further reduce the sample size from the drop-loser design by as much as 10-20%.

An Adaptive Staggered Dose Design for a Normal Endpoint

In a clinical trial where several doses are compared to a control, a multi-stage design that combines both the selection of the best dose and the confirmation of this selected dose is desirable. An example is the two-stage drop-the-losers or pick-the-winner design, where inferior doses are dropped after interim analysis. Selection of target dose(s) can be based on ranking of observed effects, hypothesis testing with adjustment for multiplicity, or other criteria at interim stages. A number of methods have been proposed and have made significant gains in trial efficiency. However, many of these designs started off with all doses with equal allocation and did not consider prioritizing the doses using existing dose-response information. We propose an adaptive staggered dose procedure that allows explicit prioritization of doses and applies error spending scheme that favors doses with assumed better responses. This design starts off with only a subset of the doses and adaptively adds new doses depending on interim results. Using simulation, we have shown that this design performs better in terms of increased statistical power than the drop-the-losers design given strong prior information of dose response.

When is a seamless study desirable? Case studies from different pharmaceutical sponsors

BACKGROUND

Inferentially seamless studies are one of the best-known adaptive trial designs. Statistical inference for these studies is a well-studied problem. Regulatory guidance suggests that statistical issues associated with study conduct are not as well understood. Some of these issues are caused by the need for early pre-specification of the phase III design and the absence of sponsor access to unblinded data. Before statisticians decide to choose a seamless IIb/III design for their programme, they should consider whether these pitfalls will be an issue for their programme.

METHODS

We consider four case studies. Each design met with varying degrees of success. We explore the reasons for this variation to identify characteristics of drug development programmes that lend themselves well to inferentially seamless trials and other characteristics that warn of difficulties.

RESULTS

Seamless studies require increased upfront investment and planning to enable the phase III design to be specified at the outset of phase II. Pivotal, inferentially seamless studies are unlikely to allow meaningful sponsor access to unblinded data before study completion. This limits a sponsor's ability to reflect new information in the phase III portion.

CONCLUSIONS

When few clinical data have been gathered about a drug, phase II data will answer many unresolved questions. Committing to phase III plans and study designs before phase II begins introduces extra risk to drug development. However, seamless pivotal studies may be an attractive option when the clinical setting and development programme allow, for example, when revisiting dose selection.

Data-driven treatment selection for seamless phase II/III trials incorporating early-outcome data

Seamless phase II/III clinical trials are conducted in two stages with treatment selection at the first stage. In the first stage, patients are randomized to a control or one of k > 1 experimental treatments. At the end of this stage, interim data are analysed, and a decision is made concerning which experimental treatment should continue to the second stage. If the primary endpoint is observable only after some period of follow-up, at the interim analysis data may be available on some early outcome on a larger number of patients than those for whom the primary endpoint is available. These early endpoint data can thus be used for treatment selection. For two previously proposed approaches, the power has been shown to be greater for one or other method depending on the true treatment effects and correlations. We propose a new approach that builds on the previously proposed approaches and uses data available at the interim analysis to estimate these parameters and then, on the basis of these estimates, chooses the treatment selection method with the highest probability of correctly selecting the most effective treatment. This method is shown to perform well compared with the two previously described methods for a wide range of true parameter values. In most cases, the performance of the new method is either similar to or, in some cases, better than either of the two previously proposed methods.

Opportunities for improving therapy development in ALS

In May 2013, The ALS Association and The Northeast ALS Consortium (NEALS) convened a meeting of stakeholders for a round-table discussion of ways to improve therapy development in ALS. The following overview summarizes issues raised and potential new directions discussed at the meeting. We recommend that future phase II clinical trials in ALS proceed when the proposed treatment is directed at targets that are likely to be involved in ALS pathogenesis in a defined subgroup of patients, and be accompanied by one or more biomarkers to track both clinical progression and pharmacodynamic engagement of the target. Innovations in trial structure and design, and greater involvement of patient advocates, may also improve trials.

Subcutaneous golimumab induces clinical response and remission in patients with moderate-to-severe ulcerative colitis

BACKGROUND & AIMS

Little is known about the efficacy of golimumab, a fully human monoclonal antibody to tumor necrosis factor (TNF) -α, for treatment of ulcerative colitis (UC). We evaluated subcutaneous golimumab induction therapy in TNF-α antagonist-naïve patients with moderate-to-severe UC despite conventional treatment.

METHODS

We integrated double-blind phase 2 dose-finding and phase 3 dose-confirmation trials in a study of 1064 adults with UC (Mayo score: 6-12; endoscopic subscore ≥ 2; 774 patients in phase 3). Patients were randomly assigned to groups given golimumab doses of 100 mg and then 50 mg (phase 2 only), 200 mg and then 100 mg, or 400 mg and then 200 mg, 2 weeks apart. The phase 3 primary end point was week-6 clinical response. Secondary end points included week-6 clinical remission, mucosal healing, and Inflammatory Bowel Disease Questionnaire (IBDQ) score change.

RESULTS

In phase 2, median changes from baseline in the Mayo score were -1.0, -3.0, -2.0, and -3.0, in the groups given placebo, 100 mg/50 mg, 200/100 mg, and 400/200 mg golimumab, respectively. In phase 3, rates of clinical response at week 6 were 51.0% and 54.9% among patients given 200 mg/100 mg and 400 mg/200 mg golimumab, respectively, vs 30.3% among those given placebo (both, P ≤ .0001). Rates of clinical remission and mucosal healing and mean changes in IBDQ scores were significantly greater in both golimumab groups vs the placebo group (P ≤ .0014, all comparisons). Rates of serious adverse events were 6.1% and 3.0%, and rates of serious infection were 1.8% and 0.5%, in the placebo and golimumab groups, respectively. One patient in the 400 mg/200 mg group died as a result of surgical complications of an ischiorectal abscess.

CONCLUSIONS

Treatment with subcutaneous golimumab induces clinical response, remission, and mucosal healing, and increases quality of life in larger percentages of patients with active UC than placebo. ClinicalTrials.gov Number: NCT00487539.

Overview, hurdles, and future work in adaptive designs: perspectives from a National Institutes of Health-funded workshop

BACKGROUND

The clinical trials community has a never-ending search for dependable and reliable ways to improve clinical research. This exploration has led to considerable interest in adaptive clinical trial designs, which provide the flexibility to adjust trial characteristics on the basis of data reviewed at interim stages. Statisticians and clinical investigators have proposed or implemented a wide variety of adaptations in clinical trials, but specific approaches have met with differing levels of support. Within industry, investigators are actively exploring the benefits and pitfalls associated with adaptive designs (ADs). For example, a Drug Information Association (DIA) working group on ADs has engaged regulatory agencies in discussions. Many researchers working on publicly funded clinical trials, however, are not yet fully engaged in this discussion. We organized the Scientific Advances in Adaptive Clinical Trial Designs Workshop to begin a conversation about using ADs in publicly funded research. Held in November of 2009, the 1½-day workshop brought together representatives from the National Institutes of Health (NIH), the Food and Drug Administration (FDA), the European Medicines Agency (EMA), the pharmaceutical industry, nonprofit foundations, the patient advocacy community, and academia. The workshop offered a forum for participants to address issues of ADs that arise at the planning, designing, and execution stages of clinical trials, and to hear the perspectives of influential members of the clinical trials community. The participants also set forth recommendations for guiding action to promote the appropriate use of ADs. These recommendations have since been presented, discussed, and vetted in a number of venues including the University of Pennsylvania Conference on Statistical Issues in Clinical Trials and the Society for Clinical Trials annual meeting.

PURPOSE

To provide a brief overview of ADs, describe the rationale behind conducting the workshop, and summarize the main recommendations that were produced as a result of this workshop.

CONCLUSIONS

There is a growing interest in the use of adaptive clinical trial designs. However, a number of logistical barriers need to be addressed in order to obtain the potential advantages of an AD. Currently, the pharmaceutical industry is well ahead of academic trialists with respect to addressing these barriers. Academic trialists will need to address important issues such as education, infrastructure, modifications to existing funding models, and the impact on Data and Safety Monitoring Boards (DSMB) in order to achieve the possible benefits of adaptive clinical trial designs.

Analysis of time-to-event data with nonuniform patient entry and loss to follow-up under a two-stage seamless adaptive design with weibull distribution

In the pharmaceutical industry, a two-stage seamless adaptive design that combines two separate independent clinical trials into a single clinical study is commonly employed in clinical research and development. In practice, in the interest of shortening the development process, it is not uncommon to consider study endpoints with different treatment durations at different stages (Chow and Chang, 2006 ; Maca et al., 2006 ). In this study, our attention is placed on the case where the study endpoints of interest are time-to-event data where the durations at the two stages are different with nonuniform patient entry and losses to follow-up or dropouts. Test statistics for the final analysis based on the combined data are developed under various hypotheses for testing equality, superiority, noninferiority, and equivalence. In addition, formulas for sample size calculation and allocation between the two stages based on the proposed test statistic are derived.

Adaptive trial designs: a review of barriers and opportunities

Adaptive designs allow planned modifications based on data accumulating within a study. The promise of greater flexibility and efficiency stimulates increasing interest in adaptive designs from clinical, academic, and regulatory parties. When adaptive designs are used properly, efficiencies can include a smaller sample size, a more efficient treatment development process, and an increased chance of correctly answering the clinical question of interest. However, improper adaptations can lead to biased studies. A broad definition of adaptive designs allows for countless variations, which creates confusion as to the statistical validity and practical feasibility of many designs. Determining properties of a particular adaptive design requires careful consideration of the scientific context and statistical assumptions. We first review several adaptive designs that garner the most current interest. We focus on the design principles and research issues that lead to particular designs being appealing or unappealing in particular applications. We separately discuss exploratory and confirmatory stage designs in order to account for the differences in regulatory concerns. We include adaptive seamless designs, which combine stages in a unified approach. We also highlight a number of applied areas, such as comparative effectiveness research, that would benefit from the use of adaptive designs. Finally, we describe a number of current barriers and provide initial suggestions for overcoming them in order to promote wider use of appropriate adaptive designs. Given the breadth of the coverage all mathematical and most implementation details are omitted for the sake of brevity. However, the interested reader will find that we provide current references to focused reviews and original theoretical sources which lead to details of the current state of the art in theory and practice.

Sample size re-estimation in an on-going NIH-sponsored clinical trial: the secondary prevention of small subcortical strokes experience

BACKGROUND AND PURPOSE

When planning clinical trials, decisions regarding sample size are often based on educated guesses of parameters, which may in fact prove to be over- or under-estimates. For example, after initiation of the SPS3 study, published data indicated that the recurrent stroke rates might be lower than initially planned for the study. Failure to account for this could result in an under-powered study. Thus, we performed a sample size re-estimation, and describe the experience herein.

METHODS

We evaluated different scenarios based on a re-estimated overall event rate, including increasing the sample size and increasing the follow-up time, to determine their impact on both type I error and the power to detect the initially planned treatment difference.

RESULTS

We found that by increasing the sample size from 2500 to 3000 and by following the patients for one year after the end of recruitment, we would maintain our planned type I error rate, and increase the power to detect the prespecified clinically meaningful difference to between 67% and 87%, depending on the rate of recruitment.

CONCLUSIONS

We successfully implemented this unplanned design modification in the SPS3 study, in order to allow for sufficient power to detect the planned treatment differences.

CLINICAL TRIALS REGISTRATION INFORMATION

Clinical Trials Registration - http://clinicaltrials.gov/show/NCT00059306. Unique identifier: NCT00059306.

Phase 2 trial design in neuro-oncology revisited: a report from the RANO group

Advances in the management of gliomas, including the approval of agents such as temozolomide and bevacizumab, have created an evolving therapeutic landscape in glioma treatment, thus affecting our ability to reliably use historical controls to comparatively assess the activity of new therapies. Furthermore, the increasing availability of novel, targeted agents--which are competing for a small patient population, in view of the low incidence of primary brain tumours--draws attention to the need to improve the efficiency of phase 2 clinical testing in neuro-oncology to expeditiously transition the most promising of these drugs or combinations to potentially practice-changing phase 3 trials. In this report from the Response Assessment in Neurooncology (RANO) group, we review phase 2 trial designs that can address these challenges and capitalise on scientific and clinical advances in brain tumour treatment in neuro-oncology to accelerate and optimise the selection of drugs deserving further testing in phase 3 trials. Although there is still a small role for single-arm and non-comparative phase 2 designs, emphasis is placed on the potential role that comparative randomised phase 2 designs--such as screening designs, selection designs, discontinuation designs, and adaptive designs, including seamless phase 2/3 designs--can have. The rational incorporation of these designs, as determined by the specific clinical setting and the trial's endpoints or goals, has the potential to substantially advance new drug development in neuro-oncology.

A paradigm shift in drug development for treatment of rare multidrug-resistant gram-negative pathogens

Multidrug-resistant (MDR) gram-negative pathogens pose a major threat to patients worldwide. Although the organisms remain relatively uncommon overall, their incidence is steadily increasing with associated increases in mortality and pharmacoeconomic impact. As evidenced by the dearth of new products in the pipeline or in clinical use, the conventional paradigm for the development of drugs against such pathogens is generally ineffectual. We advocate the need for a shift in the current paradigm and propose innovative development programs that involve implementation of a graduated approval process. The initial phase of the proposed regulatory paradigm includes early approval of a new drug based on a robust nonrandomized study, buttressed by data from concurrent controls and a pharmacokinetic-pharmacodynamic package generated from nonclinical studies. The postapproval commitment phase will include a randomized controlled trial, when disease prevalence permits, as well as continued assessment of risks and benefits under "real world" settings.

Design issues in randomized phase II/III trials

Phase II trials are used to show sufficient preliminary activity of a new treatment (in single-arm designs or randomized screening designs) or to select among treatments with demonstrated activity (in randomized selection designs). The treatments prioritized in a phase II trial are then tested definitively against a control treatment in a randomized phase III trial. Randomized phase II/III trials use an adaptive trial design that combines these two types of trials in one, with potential gains in time and reduced numbers of patients required to be treated. Two key considerations in designing a phase II/III trial are whether to suspend accrual while the phase II data mature and the choice of phase II target treatment effect. We discuss these phase II/III design parameters, give examples of phase II/III trials, and provide recommendations concerning efficient phase II/III trial designs.

Clinical development of 2NME-based oncology treatment regimens

Over the past decade many signaling pathways have been identified and implicated in cancer development. This has lead to rational drug development of many new molecules that target specific nodes on one or more signaling pathways. It is now believed that targeting key parallel or compensatory pathways may require combining two or more new molecular entities (2NMEs). This approach to drug development differs from the classic case where a single component of a new combination regimen has yet to receive FDA approval. The approach presents numerous challenges to both regulatory authorities and industry sponsors. Of course, the key challenge is the usual demonstration of both safety and efficacy of the proposed combination. In addition, however, superiority of the 2NME-based regimen over both of the individual NME-based regimens and the standard of care (SOC) must be demonstrated. If the individual NME-based regimens are not very effective, then it is desirable to demonstrate the superiority of the 2NME-based regimen as early as possible in the clinical development program so that the number of patients exposed to an ineffective regimen is minimized. In this manuscript we present several strategies for clinical development programs for a 2NME-based oncology regimen. We make recommendations regarding settings where the proposed development strategies are most well suited.

Viewpoints on the FDA draft adaptive designs guidance from the PhRMA working group

The US Food and Drug Administration has recently released a draft guidance document on adaptive clinical trials. We comment on the document from the particular perspective of the authors as members of a PhRMA working group on this topic, which has interacted with FDA personnel on adaptive trial issue during recent years. We describe the activities and prior work of our working group, and use this as a basis to discuss the content of the guidance document as it relates to several issues of current relevance, such as data monitoring processes, adaptive dose finding, so-called seamless trial designs, and sample size reestimation.