Adaptive Seamless Designs: Selection and Prospective Testing of Hypotheses

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

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

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.

New insights into adaptive enrichment designs

The transition towards personalized medicine is happening and the new experimental framework is raising several challenges, from a clinical, ethical, logistical, regulatory, and statistical perspective. To face these challenges, innovative study designs with increasing complexity have been proposed. In particular, adaptive enrichment designs are becoming more attractive for their flexibility. However, these procedures rely on an increasing number of parameters that are unknown at the planning stage of the clinical trial, so the study design requires particular care. This review is dedicated to adaptive enrichment studies with a focus on design aspects. While many papers deal with methods for the analysis, the sample size determination and the optimal allocation problem have been overlooked. We discuss the multiple aspects involved in adaptive enrichment designs that contribute to their advantages and disadvantages. The decision-making process of whether or not it is worth enriching should be driven by clinical and ethical considerations as well as scientific and statistical concerns.

Statistical and Operational Considerations for 2-Stage Adaptive Designs with Simultaneous Evaluation of Overall and Marker-Selected Populations in Oncology Confirmatory Trials

In biomarker enrichment study designs that start with an all-comer population, simultaneous evaluation of the entire and the marker-selected populations can be more desirable than pre-specifying the testing order, when the degree of marker predictiveness is uncertain. While there has been substantial research on this approach, our goal is to provide a complete overview and guidance in all aspects of this approach, including the interim analysis potentially using different endpoints, combination tests with associated multiplicity control, and the final treatment effect estimation. Regulatory/operational aspects and actual cases demonstrating the potential advantage of this approach are also described.

Adaptive Enrichment Designs in Clinical Trials

Adaptive enrichment designs for clinical trials may include rules that use interim data to identify treatment-sensitive patient subgroups, select or compare treatments, or change entry criteria. A common setting is a trial to compare a new biologically targeted agent to standard therapy. An enrichment design's structure depends on its goals, how it accounts for patient heterogeneity and treatment effects, and practical constraints. This article first covers basic concepts, including treatment-biomarker interaction, precision medicine, selection bias, and sequentially adaptive decision making, and briefly describes some different types of enrichment. Numerical illustrations are provided for qualitatively different cases involving treatment-biomarker interactions. Reviews are given of adaptive signature designs; a Bayesian design that uses a random partition to identify treatment-sensitive biomarker subgroups and assign treatments; and designs that enrich superior treatment sample sizes overall or within subgroups, make subgroup-specific decisions, or include outcome-adaptive randomization.

Adaptive enrichment trials: What are the benefits?

When planning a Phase III clinical trial, suppose a certain subset of patients is expected to respond particularly well to the new treatment. Adaptive enrichment designs make use of interim data in selecting the target population for the remainder of the trial, either continuing with the full population or restricting recruitment to the subset of patients. We define a multiple testing procedure that maintains strong control of the familywise error rate, while allowing for the adaptive sampling procedure. We derive the Bayes optimal rule for deciding whether or not to restrict recruitment to the subset after the interim analysis and present an efficient algorithm to facilitate simulation-based optimisation, enabling the construction of Bayes optimal rules in a wide variety of problem formulations. We compare adaptive enrichment designs with traditional nonadaptive designs in a broad range of examples and draw clear conclusions about the potential benefits of adaptive enrichment.

Adding dose modifications into Phase II and Phase II/III seamless trials

We present a technique for adding dose modifications into seamless Phase II and Phase II/III trials featuring dose selection at an interim analysis. The method is convenient to apply and can be used either in a fully prespecified, structured way or as a response to new considerations that emerge at interim. Strong control of the familywise error rate regarding false declarations of efficacy versus control is maintained. Two examples are given. One illustrates how the method could potentially "save" a trial performed in a Phase II context. The other is a seamless Phase II/III trial that uses an adaptive exploration strategy for an assumed nonmonotonic dose-response curve. It can result in greatly improved efficiency over a standard "promote the winner" rule.

Review of Statistical Methods for Biomarker-Driven Clinical Trials

The discovery of somatic driver mutations in kinases and receptors has stimulated the development of molecularly targeted treatments that require companion diagnostics and new approaches to clinical development. This article reviews some of the clinical trial designs that have been developed to address these opportunities, including phase II basket and platform trials as well as phase III enrichment and biomarker adaptive designs. It also re-examines some of the conventional wisdom that previously dominated clinical trial design and discusses development and internal validation of a predictive biomarker as a new paradigm for optimizing the intended-use subset for a treatment. Statistical methods now being used in adaptive biomarker-driven clinical trials are reviewed. Some previous paradigms for clinical trial design can limit the development of more effective methods on the basis of prospectively planned adaptive methods, but useful new methods have been developed for analysis of genome-wide data and for the design of adaptively enriched studies. In many cases, the heterogeneity of populations eligible for clinical trials as traditionally defined makes it unlikely that molecularly targeted treatments will be effective for a majority of the eligible patients. New methods for dealing with patient heterogeneity in therapeutic response should be used in the design of phase III clinical trials.

Using Bayesian modeling in frequentist adaptive enrichment designs

Our increased understanding of the mechanistic heterogeneity of diseases has pushed the development of targeted therapeutics. We do not expect all patients with a given disease to benefit from a targeted drug; only those in the target population. That is, those with sufficient dysregulation in the biomolecular pathway targeted by treatment. However, due to complexity of the pathway, and/or technical issues with our characterizing assay, it is often hard to characterize the target population until well into large-scale clinical trials. This has stimulated the development of adaptive enrichment trials; clinical trials in which the target population is adaptively learned; and enrollment criteria are adaptively updated to reflect this growing understanding. This paper proposes a framework for group-sequential adaptive enrichment trials. Building on the work of Simon & Simon (2013). Adaptive enrichment designs for clinical trials. Biostatistics 14(4), 613-625), it includes a frequentist hypothesis test at the end of the trial. However, it uses Bayesian methods to optimize the decisions required during the trial (regarding how to restrict enrollment) and Bayesian methods to estimate effect size, and characterize the target population at the end of the trial. This joint frequentist/Bayesian design combines the power of Bayesian methods for decision making with the use of a formal hypothesis test at the end of the trial to preserve the studywise probability of a type I error.

Inference for multimarker adaptive enrichment trials

Identification of treatment selection biomarkers has become very important in cancer drug development. Adaptive enrichment designs have been developed for situations where a unique treatment selection biomarker is not apparent based on the mechanism of action of the drug. With such designs, the eligibility rules may be adaptively modified at interim analysis times to exclude patients who are unlikely to benefit from the test treatment.We consider a recently proposed, particularly flexible approach that permits development of model-based multifeature predictive classifiers as well as optimized cut-points for continuous biomarkers. A single significance test, including all randomized patients, is performed at the end of the trial of the strong null hypothesis that the expected outcome on the test treatment is no better than control for any of the subset populations of patients accrued in the K stages of the clinical trial. In this paper, we address 2 issues involving inference following an adaptive enrichment design as described above. The first is specification of the intended use population and estimation of treatment effect for that population following rejection of the strong null hypothesis. The second issue is defining conditions in which rejection of the strong null hypothesis implies rejection of the null hypothesis for the intended use population.

Limb-Leaf designs for adaptive exploration of the dose-response curve

We propose a two-stage strategy, called the Limb-Leaf method, to explore the dose-response curve using dose promotion and addition in the context of adaptive seamless Phase II/III trials. Strong control of the overall type 1 familywise error rate of the proposed method is enforced by the closed testing principle. The design constants are determined to minimize the risk-adjusted expected total sample size while maintaining a target power. In the case of a nonmonotonic dose response curve where more doses are required to adequately explore the curve, substantial savings in sample size are achieved compared with a traditional strategy which offers only selection and promotion from among initial first stage doses.

Accurate p-values for adaptive designs with binary endpoints

Adaptive designs encompass all trials allowing various types of design modifications over the course of the trial. A key requirement for confirmatory adaptive designs to be accepted by regulators is the strong control of the family-wise error rate. This can be achieved by combining the p-values for each arm and stage to account for adaptations (including but not limited to treatment selection), sample size adaptation and multiple stages. While the theory for this is novel and well-established, in practice, these methods can perform poorly, especially for unbalanced designs and for small to moderate sample sizes. The problem is that standard stagewise tests have inflated type I error rate, especially but not only when the baseline success rate is close to the boundary and this is carried over to the adaptive tests, seriously inflating the family-wise error rate. We propose to fix this problem by feeding the adaptive test with second-order accurate p-values, in particular bootstrap p-values. Secondly, an adjusted version of the Simes procedure for testing intersection hypotheses that reduces the built-in conservatism is suggested. Numerical work and simulations show that unlike their standard counterparts the new approach preserves the overall error rate, at or below the nominal level across the board, irrespective of the baseline rate, stagewise sample sizes or allocation ratio. Copyright © 2017 John Wiley & Sons, Ltd.

Asymmetric inner wedge group sequential tests with applications to verifying whether effective drug concentrations are similar in adults and children

Extrapolating from information available on one patient group to support conclusions about another is common in clinical research. For example, the findings of clinical trials, often conducted in highly selective patient cohorts, are routinely extrapolated to wider populations by policy makers. Meanwhile, the results of adult trials may be used to support conclusions about the effects of a medicine in children. For example, if the effective concentration of a drug can be assumed to be similar in adults and children, an appropriate paediatric dosing rule may be found by 'bridging', that is, by matching the adult effective concentration. However, this strategy may result in children receiving an ineffective or hazardous dose if, in fact, effective concentrations differ between adults and children. When there is uncertainty about the equality of effective concentrations, some pharmacokinetic-pharmacodynamic data may be needed in children to verify that differences are small. In this paper, we derive optimal group sequential tests that can be used to verify this assumption efficiently. Asymmetric inner wedge tests are constructed that permit early stopping to accept or reject an assumption of similar effective drug concentrations in adults and children. Asymmetry arises because the consequences of under- and over-dosing may differ. We show how confidence intervals can be obtained on termination of these tests and illustrate the small sample operating characteristics of designs using simulation. Copyright © 2016 John Wiley & Sons, Ltd.

Group sequential designs with prospectively planned rules for subpopulation enrichment

We propose a class of randomized trial designs aimed at gaining the advantages of wider generalizability and faster recruitment while mitigating the risks of including a population for which there is greater a priori uncertainty. We focus on testing null hypotheses for the overall population and a predefined subpopulation. Our designs have preplanned rules for modifying enrollment criteria based on data accrued at interim analyses. For example, enrollment can be restricted if the participants from a predefined subpopulation are not benefiting from the new treatment. Our designs have the following features: the multiple testing procedure fully leverages the correlation among statistics for different populations; the asymptotic familywise Type I error rate is strongly controlled; for outcomes that are binary or normally distributed, the decision rule and multiple testing procedure are functions of the data only through minimal sufficient statistics. Our designs incorporate standard group sequential boundaries for each population of interest; this may be helpful in communicating the designs, because many clinical investigators are familiar with such boundaries, which can be summarized succinctly in a single table or graph. We demonstrate these designs through simulations of a Phase III trial of a new treatment for stroke. User-friendly, free software implementing these designs is described. Copyright © 2016 John Wiley & Sons, Ltd.

Biomarker-Guided Adaptive Trial Designs in Phase II and Phase III: A Methodological Review

BACKGROUND

Personalized medicine is a growing area of research which aims to tailor the treatment given to a patient according to one or more personal characteristics. These characteristics can be demographic such as age or gender, or biological such as a genetic or other biomarker. Prior to utilizing a patient's biomarker information in clinical practice, robust testing in terms of analytical validity, clinical validity and clinical utility is necessary. A number of clinical trial designs have been proposed for testing a biomarker's clinical utility, including Phase II and Phase III clinical trials which aim to test the effectiveness of a biomarker-guided approach to treatment; these designs can be broadly classified into adaptive and non-adaptive. While adaptive designs allow planned modifications based on accumulating information during a trial, non-adaptive designs are typically simpler but less flexible.

METHODS AND FINDINGS

We have undertaken a comprehensive review of biomarker-guided adaptive trial designs proposed in the past decade. We have identified eight distinct biomarker-guided adaptive designs and nine variations from 107 studies. Substantial variability has been observed in terms of how trial designs are described and particularly in the terminology used by different authors. We have graphically displayed the current biomarker-guided adaptive trial designs and summarised the characteristics of each design.

CONCLUSIONS

Our in-depth overview provides future researchers with clarity in definition, methodology and terminology for biomarker-guided adaptive trial designs.

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.

A randomized, controlled trial of oral propranolol in infantile hemangioma

BACKGROUND

Oral propranolol has been used to treat complicated infantile hemangiomas, although data from randomized, controlled trials to inform its use are limited.

METHODS

We performed a multicenter, randomized, double-blind, adaptive, phase 2-3 trial assessing the efficacy and safety of a pediatric-specific oral propranolol solution in infants 1 to 5 months of age with proliferating infantile hemangioma requiring systemic therapy. Infants were randomly assigned to receive placebo or one of four propranolol regimens (1 or 3 mg of propranolol base per kilogram of body weight per day for 3 or 6 months). A preplanned interim analysis was conducted to identify the regimen to study for the final efficacy analysis. The primary end point was success (complete or nearly complete resolution of the target hemangioma) or failure of trial treatment at week 24, as assessed by independent, centralized, blinded evaluations of standardized photographs.

RESULTS

Of 460 infants who underwent randomization, 456 received treatment. On the basis of an interim analysis of the first 188 patients who completed 24 weeks of trial treatment, the regimen of 3 mg of propranolol per kilogram per day for 6 months was selected for the final efficacy analysis. The frequency of successful treatment was higher with this regimen than with placebo (60% vs. 4%, P<0.001). A total of 88% of patients who received the selected propranolol regimen showed improvement by week 5, versus 5% of patients who received placebo. A total of 10% of patients in whom treatment with propranolol was successful required systemic retreatment during follow-up. Known adverse events associated with propranolol (hypoglycemia, hypotension, bradycardia, and bronchospasm) occurred infrequently, with no significant difference in frequency between the placebo group and the groups receiving propranolol.

CONCLUSIONS

This trial showed that propranolol was effective at a dose of 3 mg per kilogram per day for 6 months in the treatment of infantile hemangioma. (Funded by Pierre Fabre Dermatologie; ClinicalTrials.gov number, NCT01056341.).

Optimizing the data combination rule for seamless phase II/III clinical trials

We consider seamless phase II/III clinical trials that compare K treatments with a common control in phase II then test the most promising treatment against control in phase III. The final hypothesis test for the selected treatment can use data from both phases, subject to controlling the familywise type I error rate. We show that the choice of method for conducting the final hypothesis test has a substantial impact on the power to demonstrate that an effective treatment is superior to control. To understand these differences in power, we derive decision rules maximizing power for particular configurations of treatment effects. A rule with such an optimal frequentist property is found as the solution to a multivariate Bayes decision problem. The optimal rules that we derive depend on the assumed configuration of treatment means. However, we are able to identify two decision rules with robust efficiency: a rule using a weighted average of the phase II and phase III data on the selected treatment and control, and a closed testing procedure using an inverse normal combination rule and a Dunnett test for intersection hypotheses. For the first of these rules, we find the optimal division of a given total sample size between phases II and III. We also assess the value of using phase II data in the final analysis and find that for many plausible scenarios, between 50% and 70% of the phase II numbers on the selected treatment and control would need to be added to the phase III sample size in order to achieve the same increase in power. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

A seamless phase IIB/III adaptive outcome trial: design rationale and implementation challenges

BACKGROUND

The licensed four-valent prophylactic human papillomavirus vaccine is highly efficacious in preventing cervical, vulvar, vaginal, and anal cancers and related precancers caused by human papillomavirus types 6, 11, 16, and 18. These four types account for approximately 70% of cervical cancers. A nine-valent human papillomavirus vaccine, including the four original types (6, 11, 16, and 18) plus the next five most prevalent types in cervical cancer (31, 33, 45, 52, and 58) could provide approximately 90% overall cervical cancer coverage. To expedite the nine-valent human papillomavirus vaccine clinical development, an adaptive, seamless Phase IIB/III outcome trial with ∼ 15,000 subjects was conducted to facilitate dose formulation selection and provide pivotal evidence of safety and efficacy for regulatory registrations.

PURPOSE

We discuss the design rationale and implementation challenges of the outcome trial, focusing on the adaptive feature of the seamless Phase IIB/III design.

METHODS

Subjects were enrolled in two parts (Part A and Part B). Approximately 1240 women, 16-26 years of age, were enrolled in Part A for Phase IIB evaluation and equally randomized to one of three dose formulations of the nine-valent human papillomavirus vaccine or the four-valent human papillomavirus vaccine (active control). Based on an interim analysis of immunogenicity and safety, one dose formulation of the nine-valent human papillomavirus vaccine was selected for evaluation in the Phase III part of the study. Subjects enrolled in Part A who received the selected dose formulation of the nine-valent human papillomavirus vaccine or four-valent human papillomavirus vaccine continued to be followed up and contributed to the final efficacy and safety analyses. In addition, ∼ 13,400 women 16-26 years of age were enrolled in Part B, randomized to nine-valent human papillomavirus vaccine at the selected dose formulation or four-valent human papillomavirus vaccine, and followed for immunogenicity, efficacy, and safety.

RESULTS

A seamless Phase IIB/III design was justified by the extensive pre-existing knowledge of the licensed four-valent human papillomavirus vaccine and the development objectives for the nine-valent human papillomavirus vaccine. Subjects enrolled in Part A who received either the selected nine-valent human papillomavirus formulation or four-valent human papillomavirus vaccine contributed ∼ 10% of person-years of follow-up due to its earlier start-thereby maximizing the overall efficiency of the trial. Some of the challenges encountered in the implementation of the adaptive design included practical considerations during Phase IIB formulation selection by internal and external committees, End-of-Phase II discussion with health authorities and managing changes in the assay for immunological endpoints.

LIMITATIONS

Application of the experience and lesson learned from this seamless adaptive design to other clinical programs may depend on case-by-case consideration.

CONCLUSION

A seamless Phase IIB/III adaptive design was successfully implemented in this large outcome study. The development time of the second-generation nine-valent human papillomavirus vaccine was shortened due to improved statistical efficiency.

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

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

A comparison of methods for constructing confidence intervals after phase II/III clinical trials

Recently, in order to accelerate drug development, trials that use adaptive seamless designs such as phase II/III clinical trials have been proposed. Phase II/III clinical trials combine traditional phases II and III into a single trial that is conducted in two stages. Using stage 1 data, an interim analysis is performed to answer phase II objectives and after collection of stage 2 data, a final confirmatory analysis is performed to answer phase III objectives. In this paper we consider phase II/III clinical trials in which, at stage 1, several experimental treatments are compared to a control and the apparently most effective experimental treatment is selected to continue to stage 2. Although these trials are attractive because the confirmatory analysis includes phase II data from stage 1, the inference methods used for trials that compare a single experimental treatment to a control and do not have an interim analysis are no longer appropriate. Several methods for analysing phase II/III clinical trials have been developed. These methods are recent and so there is little literature on extensive comparisons of their characteristics. In this paper we review and compare the various methods available for constructing confidence intervals after phase II/III clinical trials.

Interim treatment selection with a flexible selection margin in clinical trials

When several treatment arms are administered along with a control arm in a trial, dropping the non-promising treatments at an early stage helps to save the resources and expedite the trial. In such adaptive designs with treatment selection, a common selection rule is to pick the most promising treatment, for example, the treatment with the numerically highest mean response, at the interim stage. However, with only a single treatment selected for final evaluation, this selection rule is often too inflexible. We modified this interim selection rule by introducing a flexible selection margin to judge the acceptable treatment difference. Another treatment could be selected at the interim stage in addition to the empirically best one if the differences of the observed treatment effect between them do not exceed this margin. We considered the study starting with two treatment arms and a control arm. We developed hypothesis testing procedures to assess the selected treatment(s) by taking into account the interim selection process. Compared with the one-winner selection designs, the modified selection rule makes the design more flexible and practical.

Confidence intervals for the selected population in randomized trials that adapt the population enrolled

It is a challenge to design randomized trials when it is suspected that a treatment may benefit only certain subsets of the target population. In such situations, trial designs have been proposed that modify the population enrolled based on an interim analysis, in a preplanned manner. For example, if there is early evidence during the trial that the treatment only benefits a certain subset of the population, enrollment may then be restricted to this subset. At the end of such a trial, it is desirable to draw inferences about the selected population. We focus on constructing confidence intervals for the average treatment effect in the selected population. Confidence interval methods that fail to account for the adaptive nature of the design may fail to have the desired coverage probability. We provide a new procedure for constructing confidence intervals having at least 95% coverage probability, uniformly over a large class Q of possible data generating distributions. Our method involves computing the minimum factor c by which a standard confidence interval must be expanded in order to have, asymptotically, at least 95% coverage probability, uniformly over Q. Computing the expansion factor c is not trivial, since it is not a priori clear, for a given decision rule, for which data generating distribution leads to the worst-case coverage probability. We give an algorithm that computes c, and then prove an optimality property for the resulting confidence interval procedure.

Experimental designs for small randomised clinical trials: an algorithm for choice

Background

Small clinical trials are necessary when there are difficulties in recruiting enough patients for conventional frequentist statistical analyses to provide an appropriate answer. These trials are often necessary for the study of rare diseases as well as specific study populations e.g. children. It has been estimated that there are between 6,000 and 8,000 rare diseases that cover a broad range of diseases and patients. In the European Union these diseases affect up to 30 million people, with about 50% of those affected being children. Therapies for treating these rare diseases need their efficacy and safety evaluated but due to the small number of potential trial participants, a standard randomised controlled trial is often not feasible. There are a number of alternative trial designs to the usual parallel group design, each of which offers specific advantages, but they also have specific limitations. Thus the choice of the most appropriate design is not simple.

Methods

PubMed was searched to identify publications about the characteristics of different trial designs that can be used in randomised, comparative small clinical trials. In addition, the contents tables from 11 journals were hand-searched. An algorithm was developed using decision nodes based on the characteristics of the identified trial designs.

Results

We identified 75 publications that reported the characteristics of 12 randomised, comparative trial designs that can be used in for the evaluation of therapies in orphan diseases. The main characteristics and the advantages and limitations of these designs were summarised and used to develop an algorithm that may be used to help select an appropriate design for a given clinical situation. We used examples from publications of given disease-treatment-outcome situations, in which the investigators had used a particular trial design, to illustrate the use of the algorithm for the identification of possible alternative designs.

Conclusions

The algorithm that we propose could be a useful tool for the choice of an appropriate trial design in the development of orphan drugs for a given disease-treatment-outcome situation.

Conditionally unbiased estimation in phase II/III clinical trials with early stopping for futility

Seamless phase II/III clinical trials combine traditional phases II and III into a single trial that is conducted in two stages, with stage 1 used to answer phase II objectives such as treatment selection and stage 2 used for the confirmatory analysis, which is a phase III objective. Although seamless phase II/III clinical trials are efficient because the confirmatory analysis includes phase II data from stage 1, inference can pose statistical challenges. In this paper, we consider point estimation following seamless phase II/III clinical trials in which stage 1 is used to select the most effective experimental treatment and to decide if, compared with a control, the trial should stop at stage 1 for futility. If the trial is not stopped, then the phase III confirmatory part of the trial involves evaluation of the selected most effective experimental treatment and the control. We have developed two new estimators for the treatment difference between these two treatments with the aim of reducing bias conditional on the treatment selection made and on the fact that the trial continues to stage 2. We have demonstrated the properties of these estimators using simulations.

Integrated phase II/III clinical trials in oncology: a case study

BACKGROUND

Integrated phase II/III trial designs implement the phase II and phase III aspects of oncology studies into a single trial. Despite a body of literature discussing the merits of integrated phase II/III clinical trial designs within the past two decades, implementation of this design has been limited in oncology studies.

PURPOSE

We provide a brief discussion of the potential advantages and disadvantages of integrated phase II/III clinical trial designs in oncology and provide an example of the operating characteristics of a Radiation Therapy Oncology Group (RTOG) trial.

METHODS

We review the differences among proposed integrated phase II/III designs. Then, we illustrate the use of the design in a brain tumor trial to be conducted by the RTOG and examine the impact of association between endpoints on design performance in terms of type I error, power, study duration, and expected sample size.

RESULTS

Although integrated phase II/III designs should not be used in all situations, under appropriate conditions, significant gains can be achieved when using integrated phase II/III designs, including smaller sample size, time and resources savings, and shorter study duration.

LIMITATIONS

Data submission without delay and sufficient evaluation of intermediate endpoints are assumed.

CONCLUSIONS

Although there are potential benefits in using phase II/III designs, there also may be disadvantages. We recommend running design simulations incorporating theoretical and practical issues before implementing an integrated phase II/III design.

Flexible analytical methods for adding a treatment arm mid-study to an ongoing clinical trial

It is not uncommon to have experimental drugs under different stages of development for a given disease area. Methods are proposed for use when another treatment arm is to be added mid-study to an ongoing clinical trial. Monte Carlo simulation was used to compare potential analytical approaches for pairwise comparisons through a difference in means in independent normal populations including (1) a linear model adjusting for the design change (stage effect), (2) pooling data across the stages, or (3) the use of an adaptive combination test. In the presence of intra-stage correlation (or a non-ignorable fixed stage effect), simply pooling the data will result in a loss of power and will inflate the type I error rate. The linear model approach is more powerful, but the adaptive methods allow for flexibility (re-estimating sample size). The flexibility to add a treatment arm to an ongoing trial may result in cost savings as treatments that become ready for testing can be added to ongoing studies.

Limitations of adaptive clinical trials

Adaptive designs are aimed at introducing flexibility in clinical research by allowing important characteristics of a trial to be adapted during the course of the trial based on data coming from the trial itself. Adaptive designs can be used in all phases of clinical research, from phase I to phase III. They tend to be especially useful in early development, when the paucity of prior data makes their flexibility a key benefit. The need for adaptive designs lessened as new treatments progress to later phases of development, when emphasis shifts to confirmation of hypotheses using fully prespecified, well-controlled designs.

Optimizing randomized trial designs to distinguish which subpopulations benefit from treatment

It is a challenge to evaluate experimental treatments where it is suspected that the treatment effect may only be strong for certain subpopulations, such as those having a high initial severity of disease, or those having a particular gene variant. Standard randomized controlled trials can have low power in such situations. They also are not optimized to distinguish which subpopulations benefit from a treatment. With the goal of overcoming these limitations, we consider randomized trial designs in which the criteria for patient enrollment may be changed, in a preplanned manner, based on interim analyses. Since such designs allow data-dependent changes to the population enrolled, care must be taken to ensure strong control of the familywise Type I error rate. Our main contribution is a general method for constructing randomized trial designs that allow changes to the population enrolled based on interim data using a prespecified decision rule, for which the asymptotic, familywise Type I error rate is strongly controlled at a specified level α. As a demonstration of our method, we prove new, sharp results for a simple, two-stage enrichment design. We then compare this design to fixed designs, focusing on each design's ability to determine the overall and subpopulation-specific treatment effects.

Shortcomings in the clinical evaluation of new drugs: acute myeloid leukemia as paradigm

Drugs introduced over the past 25 years have benefitted many patients with acute myeloid leukemia (AML) and provided cure for some. Still, AML remains difficult to treat, and most patients will eventually die from their disease. Therefore, novel drugs and drug combinations are under intense investigation, and promising results eagerly awaited and embraced. However, drug development is lengthy and costs are staggering. While the phase 1-phase 2-phase 3 sequence of clinical drug testing has remained inviolate for decades, it appears intrinsically inefficient, and scientific flaws have been noted by many authors. Of major concern is the high frequency of false-positive results obtained in phase 2 studies. Here, we review features of phase 2 trials in AML that may contribute to this problem, particularly lack of control groups, patient heterogeneity, selection bias, and choice of end points. Recognizing these problems and challenges should provide us with opportunities to make drug development more efficient and less costly. We also suggest strategies for trial design improvement. Although our focus is on the treatment of AML, the principles that we highlight should be broadly applicable to the evaluation of new treatments for a variety of diseases.

Group sequential and adaptive designs - a review of basic concepts and points of discussion

In recent times, group sequential and adaptive designs for clinical trials have attracted great attention from industry, academia and regulatory authorities. These designs allow analyses on accumulating data - as opposed to classical, "fixed-sample" statistics. The rapid development of a great variety of statistical procedures is accompanied by a lively debate on their potential merits and shortcomings. The purpose of this review article is to ease orientation in both respects. First, we provide a concise overview of the essential technical concepts, with special emphasis on their interrelationships. Second, we give a structured review of the current controversial discussion on practical issues, opportunities and challenges of these new designs.