A regulatory view on adaptive/flexible clinical trial design

Phase II Trials in Drug Development and Adaptive Trial Design

Phase II clinical studies represent a critical point in determining drug costs, and phase II is a poor predictor of drug success: >30% of drugs entering phase II studies fail to progress, and >58% of drugs go on to fail in phase III. Adaptive clinical trial design has been proposed as a way to reduce the costs of phase II testing by providing earlier determination of futility and prediction of phase III success, reducing overall phase II and III trial sizes, and shortening overall drug development time. This review examines issues in phase II testing and adaptive trial design.

Considerations for adaptive design in pediatric clinical trials: study protocol for a systematic review, mixed-methods study, and integrated knowledge translation plan

Background

Although children have historically been excluded from clinical trials (CTs), many require medicines tested and approved in CTs, forcing health care providers to treat their pediatric patients based on extrapolated data. Unfortunately, traditional randomized CTs can be slow and resource-intensive, and they often require multi-center collaboration. However, an adaptive design (AD) framework for CTs could be used to increase the efficiency of pediatric CTs by incorporating prospectively planned modifications to CT methods without undermining the integrity or validity of the study. There are many possible adaptations, but each will have ethical, logistical, and statistical implications. It remains unclear which adaptations (or combinations thereof) will lead to real-world improvements in pediatric CT efficiency. This study will identify, evaluate, and synthesize the various regulatory, ethical, logistical, and statistical considerations and emerging issues of AD in CTs that could be used to evaluate the use of drugs in children.

Methods/design

Following the development of a peer-reviewed search strategy, a systematic review on AD in CTs will be conducted. Data on regulatory, ethical, logistic, and statistical considerations as well as population and trial design characteristics will be synthesized. A mixed-methods study including surveys and focus groups with regulators, research ethics board members, biostatisticians, clinicians, and scientists, as well as representatives from patient groups and the public will evaluate the opportunities and challenges in applying AD in trials enrolling children and propose recommendations on best practices.

Discussion

This study will deliver practical recommendations on the use of AD in pediatric CTs. Collaboration and consultation with national and global partners will ensure that our results meet the needs of researchers, regulators, and patients, both locally and globally, and that they remain current and relevant by engaging a wide variety of stakeholders. Overall, this research will enrich the knowledge base regarding if, how, and when AD can be used to answer research questions with fewer resources while still meeting the highest ethical standards and regulatory requirements for CTs. In turn, this will result in increased high-quality clinical research needed by health care providers so they have access to appropriate, population-specific evidence regarding the safe and effective use of medicines in children.

Adaptive design clinical trials: a review of the literature and ClinicalTrials.gov

OBJECTIVES

This review investigates characteristics of implemented adaptive design clinical trials and provides examples of regulatory experience with such trials.

DESIGN

Review of adaptive design clinical trials in EMBASE, PubMed, Cochrane Registry of Controlled Clinical Trials, Web of Science and ClinicalTrials.gov. Phase I and seamless Phase I/II trials were excluded. Variables extracted from trials included basic study characteristics, adaptive design features, size and use of independent data monitoring committees (DMCs) and blinded interim analyses. We also examined use of the adaptive trials in new drug submissions to the Food and Drug Administration (FDA) and European Medicines Agency (EMA) and recorded regulators' experiences with adaptive designs.

RESULTS

142 studies met inclusion criteria. There has been a recent growth in publicly reported use of adaptive designs among researchers around the world. The most frequently appearing types of adaptations were seamless Phase II/III (57%), group sequential (21%), biomarker adaptive (20%), and adaptive dose-finding designs (16%). About one-third (32%) of trials reported an independent DMC, while 6% reported blinded interim analysis. We found that 9% of adaptive trials were used for FDA product approval consideration, and 12% were used for EMA product approval consideration. International regulators had mixed experiences with adaptive trials. Many product applications with adaptive trials had extensive correspondence between drug sponsors and regulators regarding the adaptive designs, in some cases with regulators requiring revisions or alterations to research designs.

CONCLUSIONS

Wider use of adaptive designs will necessitate new drug application sponsors to engage with regulatory scientists during planning and conduct of the trials. Investigators need to more consistently report protections intended to preserve confidentiality and minimise potential operational bias during interim analysis.

Designing fecal microbiota transplant trials that account for differences in donor stool efficacy

Fecal microbiota transplantation is a highly effective intervention for patients suffering from recurrent Clostridium difficile, a common hospital-acquired infection. Fecal microbiota transplantation’s success as a therapy for C. difficile has inspired interest in performing clinical trials that experiment with fecal microbiota transplantation as a therapy for other conditions like inflammatory bowel disease, obesity, diabetes, and Parkinson’s disease. Results from clinical trials that use fecal microbiota transplantation to treat inflammatory bowel disease suggest that, for at least one condition beyond C. difficile, most fecal microbiota transplantation donors produce stool that is not efficacious. The optimal strategies for identifying and using efficacious donors have not been investigated. We therefore examined the optimal Bayesian response-adaptive strategy for allocating patients to donors and formulated a computationally tractable myopic heuristic. This heuristic computes the probability that a donor is efficacious by updating prior expectations about the efficacy of fecal microbiota transplantation, the placebo rate, and the fraction of donors that produce efficacious stool. In simulations designed to mimic a recent fecal microbiota transplantation clinical trial, for which traditional power calculations predict [Formula: see text] statistical power, we found that accounting for differences in donor stool efficacy reduced the predicted statistical power to [Formula: see text]. For these simulations, using the heuristic Bayesian allocation strategy more than quadrupled the statistical power to [Formula: see text]. We use the results of similar simulations to make recommendations about the number of patients, the number of donors, and the choice of clinical endpoint that clinical trials should use to optimize their ability to detect if fecal microbiota transplantation is effective for treating a condition.

Attitudes and opinions regarding confirmatory adaptive clinical trials: a mixed methods analysis from the Adaptive Designs Accelerating Promising Trials into Treatments (ADAPT-IT) project

Background

Adaptive designs have been increasingly used in the pharmaceutical and device industries, but adoption within the academic setting has been less widespread — particularly for confirmatory phase trials. We sought to understand perceptions about understanding, acceptability, and scientific validity of adaptive clinical trials (ACTs).

Methods

We used a convergent mixed methods design using survey and mini-focus group data collection procedures to elucidate attitudes and opinions among “trial community” stakeholders regarding understanding, acceptability, efficiency, scientific validity, and speed of discovery with adaptive designs. Data were collected about various aspects of ACTs using self-administered surveys (paper or Web-based) with visual analog scales (VASs) with free text responses and with mini-focus groups of key stakeholders. Participants were recruited as part of an ongoing NIH/FDA-funded research project exploring the incorporation of ACTs into an existing NIH network that focuses on confirmatory phase clinical trials in neurological emergencies. “Trial community” representatives, namely, clinical investigators, biostatisticians, NIH officials, and FDA scientists involved in the planning of four clinical trials, were eligible to participate. In addition, recent and current members of a clinical trial-oriented NIH study section were also eligible.

Results

A total of 76 stakeholders completed the survey (out of 91 who were offered it, response rate 84 %). While the VAS attitudinal data showed substantial variability across respondents about acceptability and understanding of ACTs by various constituencies, respondents perceived clinicians to be less likely to understand ACTs and that ACTs probably would increase the efficiency of discovery. Textual and focus group responses emerged into several themes that enhanced understanding of VAS attitudinal data including the following: acceptability of adaptive designs depends on constituency and situation; there is variable understanding of ACTs (limited among clinicians, perceived to be higher at FDA); views about the potential for efficiency depend on the situation and implementation. Participants also frequently mentioned a need for greater education within the academic community. Finally, the empiric, non-quantitative selection of treatments for phase III trials based on limited phase II trials was highlighted as an opportunity for improvement and a potential explanation for the high number of neutral confirmatory trials.

Conclusions

These data show considerable variations in attitudes and beliefs about ACTs among trial community representatives. For adaptive trials to be fully considered when appropriate and for the research enterprise to realize the full potential of adaptive designs will likely require extensive experience and trust building within the trial community.

Electronic supplementary material

The online version of this article (doi:10.1186/s13063-016-1493-z) contains supplementary material, which is available to authorized users.

Methods for flexible sample-size design in clinical trials: Likelihood, weighted, dual test, and promising zone approaches

Sample size plays a crucial role in clinical trials. Flexible sample-size designs, as part of the more general category of adaptive designs that utilize interim data, have been a popular topic in recent years. In this paper, we give a comparative review of four related methods for such a design. The likelihood method uses the likelihood ratio test with an adjusted critical value. The weighted method adjusts the test statistic with given weights rather than the critical value. The dual test method requires both the likelihood ratio statistic and the weighted statistic to be greater than the unadjusted critical value. The promising zone approach uses the likelihood ratio statistic with the unadjusted value and other constraints. All four methods preserve the type-I error rate. In this paper we explore their properties and compare their relationships and merits. We show that the sample size rules for the dual test are in conflict with the rules of the promising zone approach. We delineate what is necessary to specify in the study protocol to ensure the validity of the statistical procedure and what can be kept implicit in the protocol so that more flexibility can be attained for confirmatory phase III trials in meeting regulatory requirements. We also prove that under mild conditions, the likelihood ratio test still preserves the type-I error rate when the actual sample size is larger than the re-calculated one.

The Potential of Adaptive Design in Animal Studies

Clinical trials are the backbone of medical research, and are often the last step in the development of new therapies for use in patients. Prior to human testing, however, preclinical studies using animal subjects are usually performed in order to provide initial data on the safety and effectiveness of prospective treatments. These studies can be costly and time consuming, and may also raise concerns about the ethical treatment of animals when potentially harmful procedures are involved. Adaptive design is a process by which the methods used in a study may be altered while it is being conducted in response to preliminary data or other new information. Adaptive design has been shown to be useful in reducing the time and costs associated with clinical trials, and may provide similar benefits in preclinical animal studies. The purpose of this review is to summarize various aspects of adaptive design and evaluate its potential for use in preclinical research.

Neuroprotection in acute brain injury: an up-to-date review

Neuroprotective strategies that limit secondary tissue loss and/or improve functional outcomes have been identified in multiple animal models of ischemic, hemorrhagic, traumatic and nontraumatic cerebral lesions. However, use of these potential interventions in human randomized controlled studies has generally given disappointing results. In this paper, we summarize the current status in terms of neuroprotective strategies, both in the immediate and later stages of acute brain injury in adults. We also review potential new strategies and highlight areas for future research.

Choosing inclusion criteria that minimize the time and cost of clinical trials

AIM: To present statistical tools to model and optimize the cost of a randomized clinical trial as a function of the stringency of patient inclusion criteria.

METHODS: We consider a two treatment, dichotomous outcome trial that includes a proportion of patients who are strong responders to the tested intervention. Patients are screened for inclusion using an arbitrary number of test results that are combined into an aggregate suitability score. The screening score is regarded as a diagnostic test for the responsive phenotype, having a specific cutoff value for inclusion and a particular sensitivity and specificity. The cutoff is a measure of stringency of inclusion criteria. Total cost is modeled as a function of the cutoff value, number of patients screened, the number of patients included, the case occurrence rate, response probabilities for control and experimental treatments, and the trial duration required to produce a statistically significant result with a specified power. Regression methods are developed to estimate relevant model parameters from pilot data in an adaptive trial design.

RESULTS: The patient numbers and total cost are strongly related to the choice of the cutoff for inclusion. Clear cost minimums exist between 5.6 and 6.1 on a representative 10-point scale of exclusiveness. Potential cost savings for typical trial scenarios range in millions of dollars. As the response rate for controls approaches 50%, the proper choice of inclusion criteria can mean the difference between a successful trial and a failed trial.

CONCLUSION: Early formal estimation of optimal inclusion criteria allows planning of clinical trials to avoid high costs, excessive delays, and moral hazards of Type II errors.

Application of routine electronic health record databases for pharmacogenetic research

Inter-individual variability in drug responses is a common problem in pharmacotherapy. Several factors (non-genetic and genetic) influence drug responses in patients. When aiming to obtain an optimal benefit-risk ratio of medicines and with the emergence of genotyping technology, pharmacogenetic studies are important for providing recommendations on drug treatments. Advances in electronic healthcare information systems can contribute to increasing the quality and efficiency of such studies. This review describes the definition of pharmacogenetics, gene selection and study design for pharmacogenetic research. It also summarizes the potential of linking pharmacoepidemiology and pharmacogenetics (along with its strengths and limitations) and provides examples of pharmacogenetic studies utilizing electronic health record databases.

An adaptive two-stage dose-response design method for establishing proof of concept

We propose an adaptive two-stage dose-response design where a prespecified adaptation rule is used to add and/or drop treatment arms between the stages. We extend the multiple comparison procedures-modeling (MCP-Mod) approach into a two-stage design. In each stage, we use the same set of candidate dose-response models and test for a dose-response relationship or proof of concept (PoC) via model-associated statistics. The stage-wise test results are then combined to establish "global" PoC using a conditional error function. Our simulation studies showed good and more robust power in our design method compared to conventional and fixed designs.

Population and target considerations for triple-negative breast cancer clinical trials

Triple-negative breast cancer (TNBC) is an aggressive disease subtype that has a poor prognosis. Extensive epidemiological evidence demonstrates clear socioeconomic and demographic associations with increased likelihood of TNBC in both poorer and minority populations. Thus, biological aggressiveness with few known therapeutic directions generates disparities in breast cancer outcomes for vulnerable populations. Emerging molecular evidence of potential targets in triple-negative subpopulations offers great potential for future clinical trial directions. However, trials must appropriately consider populations at risk for aggressive subtypes of disease in order to address this disparity most completely. New US FDA draft guidance documents provide both flexible outcomes for accelerated approvals as well as flexibility in design with adaptive trials. Careful planning with design, potential patient population and choices of molecular targets informed by biomarkers will be critical to address TNBC clinical care.

An overview of the adaptive designs accelerating promising trials into treatments (ADAPT-IT) project

Randomized clinical trials, which aim to determine the efficacy and safety of drugs and medical devices, are a complex enterprise with myriad challenges, stakeholders, and traditions. Although the primary goal is scientific discovery, clinical trials must also fulfill regulatory, clinical, and ethical requirements. Innovations in clinical trials methodology have the potential to improve the quality of knowledge gained from trials, the protection of human subjects, and the efficiency of clinical research. Adaptive clinical trial methods represent a broad category of innovations intended to address a variety of long-standing challenges faced by investigators, such as sensitivity to previous assumptions and delayed identification of ineffective treatments. The implementation of adaptive clinical trial methods, however, requires greater planning and simulation compared with a more traditional design, along with more advanced administrative infrastructure for trial execution. The value of adaptive clinical trial methods in exploratory phase (phase 2) clinical research is generally well accepted, but the potential value and challenges of applying adaptive clinical trial methods in large confirmatory phase clinical trials are relatively unexplored, particularly in the academic setting. In the Adaptive Designs Accelerating Promising Trials Into Treatments (ADAPT-IT) project, a multidisciplinary team is studying how adaptive clinical trial methods could be implemented in planning actual confirmatory phase trials in an established, National Institutes of Health-funded clinical trials network. The overarching objectives of ADAPT-IT are to identify and quantitatively characterize the adaptive clinical trial methods of greatest potential value in confirmatory phase clinical trials and to elicit and understand the enthusiasms and concerns of key stakeholders that influence their willingness to try these innovative strategies.

Systems pharmacology, pharmacogenetics, and clinical trial design in network medicine

The rapidly growing disciplines of systems biology and network science are now poised to meet the fields of clinical medicine and pharmacology. Principles of systems pharmacology can be applied to drug design and, ultimately, testing in human clinical trials. Rather than focusing exclusively on single drug targets, systems pharmacology examines the holistic response of a phenotype-dependent pathway or pathways to drug perturbation. Knowledge of individual pharmacogenetic profiles further modulates the responses to these drug perturbations, moving the field toward more individualized ('personalized') drug development. The speed with which the information required to assess these system responses and their genomic underpinnings is changing and the importance of identifying the optimal drug or drug combinations for maximal benefit and minimal risk require that clinical trial design strategies be adaptable. In this paper, we review the tenets of adaptive clinical trial design as they may apply to an era of expanding knowledge of systems pharmacology and pharmacogenomics, and clinical trail design in network medicine.

Potential of adaptive clinical trial designs in pharmacogenetic research

Adaptive trial designs can be beneficial in pharmacogenetic research when prior uncertainty exists regarding the exact role and clinical relevance of genetic variability in drug response. This type of design enables us to learn about the effect of the genetic variability on drug response and to immediately use this information for the remainder of the study. For different types of adaptive trial designs, we discuss when and how the designs are suitable for pharmacogenetic research: adaptation of randomization, adaptation of patient enrollment and adaptive enrichment. To illustrate the potential benefits of an adaptive design over a fixed design, we simulated an adaptive trial based on the results of the IPASS trial. With a simple model we show that for this example an adaptive enrichment design would have led to a smaller trial, with less EGF receptor mutation-negative patients unnecessarily exposed to the drug, without compromising the α level or reducing power.

Adaptive designs for single-arm phase II trials in oncology

Clinical phase II trials in oncology are conducted to determine whether the activity of a new anticancer treatment is promising enough to merit further investigation. Two-stage designs are commonly used for this situation to allow for early termination. Designs proposed in the literature so far have the common drawback that the sample sizes for the two stages have to be specified in the protocol and have to be adhered to strictly during the course of the trial. As a consequence, designs that allow a higher extent of flexibility are desirable. In this article, we propose a new adaptive method that allows an arbitrary modification of the sample size of the second stage using the results of the interim analysis or external information while controlling the type I error rate. If the sample size is not changed during the trial, the proposed design shows very similar characteristics to the optimal two-stage design proposed by Chang et al. (Biometrics 1987; 43:865-874). However, the new design allows the use of mid-course information for the planning of the second stage, thus meeting practical requirements when performing clinical phase II trials in oncology.

Adaptive trial design: could we use this approach to improve clinical trials in the field of global health?

We need more clinical trials in the world's poorest regions to evaluate new drugs and vaccines, and also to find better ways to manage health issues. Clinical trials are expensive, time consuming, and cumbersome. However, in wealthier regions these limiting factors are being addressed to make trials less administrative and improve the designs. A good example is adaptive trial design. This innovation is becoming accepted by the regulators and has been taken up by the pharmaceutical industry to reduce product development times and costs. If this approach makes trials easier and less expensive surely we should be implementing this approach in the field of tropical medicine and international health? As yet this has rarely been proposed and there are few examples. There is a need for raising the awareness of these design approaches because they could be used to make dramatic improvements to clinical research in developing countries.

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.

Adaptive trial designs

We review adaptive designs for clinical trials, giving special attention to the control of the Type I error in late-phase confirmatory trials, when the trial planner wishes to adjust the final sample size of the study in response to an unblinded analysis of interim estimates of treatment effects. We point out that there is considerable inefficiency in using the adaptive designs that employ conditional power calculations to reestimate the sample size and that maintain the Type I error by using certain weighted test statistics. Although these adaptive designs have little advantage over familiar group-sequential designs, our review also describes recent developments in adaptive designs that are both flexible and efficient. We also discuss the use of Bayesian designs, when the context of use demands control over operating characteristics (Type I and II errors) and correction of the bias of estimated treatment effects.

Adaptive designs in clinical trials

In addition to the expensive and lengthy process of developing a new medicine, the attrition rate in clinical research was on the rise, resulting in stagnation in the development of new compounds. As a consequence to this, the US Food and Drug Administration released a critical path initiative document in 2004, highlighting the need for developing innovative trial designs. One of the innovations suggested the use of adaptive designs for clinical trials. Thus, post critical path initiative, there is a growing interest in using adaptive designs for the development of pharmaceutical products. Adaptive designs are expected to have great potential to reduce the number of patients and duration of trial and to have relatively less exposure to new drug. Adaptive designs are not new in the sense that the task of interim analysis (IA)/review of the accumulated data used in adaptive designs existed in the past too. However, such reviews/analyses of accumulated data were not necessarily planned at the stage of planning clinical trial and the methods used were not necessarily compliant with clinical trial process. The Bayesian approach commonly used in adaptive designs was developed by Thomas Bayes in the 18th century, about hundred years prior to the development of modern statistical methods by the father of modern statistics, Sir Ronald A. Fisher, but the complexity involved in Bayesian approach prevented its use in real life practice. The advances in the field of computer and information technology over the last three to four decades has changed the scenario and the Bayesian techniques are being used in adaptive designs in addition to other sequential methods used in IA. This paper attempts to describe the various adaptive designs in clinical trial and views of stakeholders about feasibility of using them, without going into mathematical complexities.

Flexible design clinical trial methodology in regulatory applications

Adaptive designs or flexible designs in a broader sense have increasingly been considered in planning pivotal registration clinical trials. Sample size reassessment design and adaptive selection design are two of such designs that appear in regulatory applications. At the design stage, consideration of sample size reassessment at an interim time of the trial should lead to extensive discussion about how to appropriately size the trial. Additionally, careful attention needs to be paid to the issue of how the size of the trial is impacted by the requirement that the final p-value of the trial meets the specific threshold of a clinically meaningful effect. These issues are not straightforward and will be discussed in this work. In a trial design that allows selection between a pre-specified patient subgroup and the initially planned overall patient population based on the accumulating data, there is an issue of what the 'overall' population means. In addition, it is critically important to know how such selection influences the validity of statistical inferences on the potentially modified overall population. This work presents the biases that may incur under adaptive patient selection designs.

Interpreting trial results in light of conflicting evidence: a Bayesian analysis of adjuvant chemotherapy for non-small-cell lung cancer

PURPOSE

When successive randomized trials contradict prior evidence, clinicians may be unsure how to evaluate them: Does accumulating evidence warrant changing practice? An increasingly popular solution, Bayesian statistics quantitatively evaluate new results in context. This study provides a clinically relevant example of Bayesian methods.

METHODS

Three recent non-small-cell lung cancer adjuvant chemotherapy trials were evaluated in light of prior conflicting data. Results were used from International Adjuvant Lung Trial (IALT), JBR.10, and Adjuvant Navelbine International Trialist Association (ANITA). Prior evidence was sequentially updated to calculate the probability of each survival benefit level (overall and by stage) and variance. Sensitivity analysis was performed using expert opinion and uninformed estimates of survival benefit prior probability.

RESULTS

The probability of a 4% survival benefit increased from 33% before IALT to 64% after IALT. After sequential updating with JBR.10 and ANITA, this probability was 82% (hazard ratio = 0.84; 95% CI, 0.77 to 0.91). IALT produced the largest decrease in variance (61%) and decreased the chance of survival decrement to 0%. Sensitivity analysis did not support a survival benefit after IALT. However, sequential updating substantiated a 4% survival benefit and, for stage II and III, more than 90% probability of a 6% benefit and 50% probability of a 12% benefit.

CONCLUSION

When evaluated in context with prior data, IALT did not support a 4% survival benefit. However, sequential updating with JBR.10 and ANITA did. A model for future assessments, this study demonstrates the unique ability of Bayesian analysis to evaluate results that contradict prior evidence.

Optimizing phase II of drug development for disease-modifying compounds

Phase II proof of concept (POC) (IIa) and dose-finding (IIb) studies represent major challenges in drug development. Prolonged development times delay effective therapies from reaching patients in need and adversely affect industry goals of decreasing time to market. Biomarkers including magnetic resonance imaging, cerebrospinal fluid tau and amyloid beta, and amyloid positron emission tomography have been considered as alternative outcomes to clinical measures. None of these is yet validated. Population enrichment is another possible solution to POC studies. More rapid progression to prespecified milestones can be achieved by enriching the population with risk factors. Conclusions based on enriched populations must be extrapolated with caution. Clinical measures with greater sensitivity than standard trial instruments might represent another strategy applicable to POC studies. Adaptive dose-response designs are being considered as a means of shortening phase IIb studies and creating a seamless interface with phase III. None of these strategies have been validated in a successful drug development program; all have some promise for reforming phase II and answering the central question of "how much information is sufficient to proceed to phase III without excessive risk for failure?"

Novel trial design: a report from the 19th frontiers symposium of ACCP

Novel approaches to drug development along with incentives to reward risk undertaken during early drug development have been proposed to improve the medical product development process. The American College of Clinical Pharmacology held its 19th Frontiers Symposium in mid-2007 to provide a forum to share new knowledge about disease progression models and adaptive and other novel trial designs. These topics reflect the changing paradigm of drug development and regulatory innovation as outlined in the Food and Drug Administration's Critical Path Initiative. This meeting report summarizes some of the presentations and panel discussions among academic, industrial, and regulatory participants regarding clinical trial design, including adaptive Bayesian approaches.

Statistical considerations for testing multiple endpoints in group sequential or adaptive clinical trials

Many clinical trials are designed with a fixed sample size or total number of events to detect a postulated size of treatment effect on a primary efficacy endpoint. When the trial is completed and the primary efficacy endpoint achieves statistical significance, formal statistical testing of other clinically important secondary endpoints often follows in order for the statistically and clinically significant results of these endpoints to be included in the label of the test pharmaceutical product. In conventional fixed designs without any interim analysis or trial extension, these endpoints are often tested in a pre-specified hierarchical order, following the closed testing principle. This testing strategy ensures a strong control of the overall type I error. However, when trials are conducted using a group-sequential design with interim analyses or can be extended using an adaptive design with an increase of sample size or total number of events, this conventional hierarchical testing strategy may violate the closure principle and the overall type I error rate may not be controlled in the strong sense.

Innovative approaches in drug development

This is the white paper on Innovative Approaches in Drug Development developed by the Biotechnology Industry Organization's (BIO) Clinical Trial Designs Workgroup. As recognized by the Food and Drug Administration's Critical Path Opportunities Report, the need to develop and apply innovative approaches to create new trial designs and clinical development programs is rapidly on the rise. Such novel approaches hold tremendous potential in the ability to refine mechanisms lying at the fundamental core of product safety and efficacy. The paper addresses the opportunities, challenges in pre-clinical and clinical trial design innovations; emphasizes the importance of safety database; and makes recommendations for carrying out the innovative approaches. The views expressed here in are solely those of the authors and do not reflect the official views of the Biotechnology Industry Organization.

Multiplicity and flexibility in clinical trials

Flexible designs offer a large amount of flexibility in clinical trials with control of the type I error rate. This allows the combination of trials from different clinical phases of a drug development process. Such combinations require designs where hypotheses are selected and/or added at interim analysis without knowing the selection rule in advance so that both flexibility and multiplicity issues arise. The paper reviews the basic principles and some of the common methods for reaching flexibility while controlling the family-wise error rate in the strong sense. Flexible designs have been criticized because they may lead to different weights for the patients from the different stages when reassessing sample sizes. Analyzing the data in a conventional way avoids such unequal weighting but may inflate the multiple type I error rate. In cases where the conditional type I error rates of the new design (and conventional analysis) are below the conditional type I error rates of the initial design the conventional analysis may, however, be done without inflating the type I error rate. Focusing on a parallel group design with two treatments and a common control, we use this principle to investigate when we can select one treatment, reassess sample sizes and test the corresponding null hypotheses by the conventional level alpha z-test without compromising on the multiple type I error rate.

Approaches to evaluation of treatment effect in randomized clinical trials with genomic subset

With the advances in human genomic/genetic studies, the clinical trial community gradually recognizes that phenotypically homogeneous patients may be heterogeneous at the genomic level. The genomic technology brings a possible avenue for developing a genomic (composite) biomarker to predict a genomically responsive patient subset that may have a (much) higher likelihood of benefiting from a treatment. Randomized controlled trial is the mainstay to provide scientifically convincing evidence of a purported effect a new treatment may demonstrate. In conventional clinical trials, the primary clinical hypothesis pertains to the therapeutic effect in all patients who are eligible for the study defined by the primary efficacy endpoint. The aspect of one-size-fits-all surrounding the conventional design has been challenged, particularly when the diseases may be heterogeneous due to observable clinical characteristics and/or unobservable underlying the genomic characteristics. Extension from the conventional single population design objective to an objective that encompasses two possible patient populations will allow more informative evaluation in the patients having different degrees of responsiveness to medication. Building in conventional clinical trials, an additional genomic objective can generate an appealing conceptual framework from the patient's perspective in addressing personalized medicine in well-controlled clinical trials. There are many perceived benefits of personalized medicine that are based on the notion of being genomically proactive in the identification of disease and prevention of disease or recurrence. In this paper, we show that an adaptive design approach can be constructed to study a clinical hypothesis of overall treatment effect and a hypothesis of treatment effect in a genomic subset more efficiently than the conventional non-adaptive approach.

An overview of statistical approaches for adaptive designs and design modifications

With adaptive design methods for clinical trials, design elements such as sample size or further interim sample sizes may be changed during the course of the trial depending on all previously collected data. The focus of the overview is on group sequential approaches where the types of adaptations need not be specified in advance. An overview of the different statistical approaches for adaptive design methods proposed in the literature is given, relations between these methods are described and some perspectives of application and for future research are discussed.

Methodological developments vs. regulatory requirements

This is a discussion of the following three papers appearing in this special issue on adaptive designs: 'FDA's critical path initiative: A perspective on contributions of biostatistics' by Robert T. O'Neill, 'A regulatory view on adaptive/flexible clinical trial design' by H. M. James Hung, Robert T. O'Neill, Sue-Jane Wang and John Lawrence; and 'Confirmatory clinical trials with an adaptive design' by Armin Koch.

Confirmatory seamless phase II/III clinical trials with hypotheses selection at interim: applications and practical considerations

Adaptive seamless phase II/III designs combine a phase II and a phase III study into one single confirmatory clinical trial. Several examples of such designs are presented, where the primary endpoint is binary, time-to-event or continuous. The interim adaptations considered include the selection of treatments and the selection of hypotheses related to a pre-specified subgroup of patients. Practical aspects concerning the planning and implementation of adaptive seamless confirmatory studies are also discussed.

Adaptive designs from the viewpoint of an academic biostatistician

This is a discussion of the following three papers appearing in this special issue on adaptive designs: 'FDA's critical path initiative: A perspective on contributions of biostatistics' by Robert T. O'Neill, 'A regulatory view on adaptive/flexible clinical trial design' by H. M. James Hung, Robert T. O'Neill, Sue-Jane Wang and John Lawrence; and 'Confirmatory clinical trials with an adaptive design' by Armin Koch.

Opening the adaptive toolbox

This is a discussion of the following three papers appearing in this special issue on adaptive designs: 'A regulatory view on adaptive/flexible clinical trial design' by H. M. James Hung, Robert T. O'Neill, Sue-Jane Wang and John Lawrence; 'Confirmatory clinical trials with an adaptive design' by Armin Koch; and 'FDA's critical path initiative: A perspective on contributions of biostatistics' by Robert T. O'Neill.

A consultant's perspective on the regulatory hurdles to adaptive trials

This is a discussion of the following two papers appearing in this special issue on adaptive designs: 'A regulatory view on adaptive/flexible clinical trial design' by H. M. James Hung, Robert T. O'Neill, Sue-Jane Wang and John Lawrence and 'Confirmatory clinical trials with an adaptive design' by Armin Koch.