Extension of a conditional performance score for sample size recalculation rules to the setting of binary endpoints

BACKGROUND

Sample size calculation is a central aspect in planning of clinical trials. The sample size is calculated based on parameter assumptions, like the treatment effect and the endpoint's variance. A fundamental problem of this approach is that the true distribution parameters are not known before the trial. Hence, sample size calculation always contains a certain degree of uncertainty, leading to the risk of underpowering or oversizing a trial. One way to cope with this uncertainty are adaptive designs. Adaptive designs allow to adjust the sample size during an interim analysis. There is a large number of such recalculation rules to choose from. To guide the choice of a suitable adaptive design with sample size recalculation, previous literature suggests a conditional performance score for studies with a normally distributed endpoint. However, binary endpoints are also frequently applied in clinical trials and the application of the conditional performance score to binary endpoints is not yet investigated.

METHODS

We extend the theory of the conditional performance score to binary endpoints by suggesting a related one-dimensional score parametrization. We moreover perform a simulation study to evaluate the operational characteristics and to illustrate application.

RESULTS

We find that the score definition can be extended without modification to the case of binary endpoints. We represent the score results by a single distribution parameter, and therefore derive a single effect measure, which contains the difference in proportions [Formula: see text] between the intervention and the control group, as well as the endpoint proportion [Formula: see text] in the control group.

CONCLUSIONS

This research extends the theory of the conditional performance score to binary endpoints and demonstrates its application in practice.

Current state-of-the-art and gaps in platform trials: 10 things you should know, insights from EU-PEARL

Platform trials bring the promise of making clinical research more efficient and more patient centric. While their use has become more widespread, including their prominent role during the COVID-19 pandemic response, broader adoption of platform trials has been limited by the lack of experience and tools to navigate the critical upfront planning required to launch such collaborative studies. The European Union-Patient-cEntric clinicAl tRial pLatform (EU-PEARL) initiative has produced new methodologies to expand the use of platform trials with an overarching infrastructure and services embedded into Integrated Research Platforms (IRPs), in collaboration with patient representatives and through consultation with U.S. Food and Drug Administration and European Medicines Agency stakeholders. In this narrative review, we discuss the outlook for platform trials in Europe, including challenges related to infrastructure, design, adaptations, data sharing and regulation. Documents derived from the EU-PEARL project, alongside a literature search including PubMed and relevant grey literature (e.g., guidance from regulatory agencies and health technology agencies) were used as sources for a multi-stage collaborative process through which the 10 more important points based on lessons drawn from the EU-PEARL project were developed and summarised as guidance for the setup of platform trials. We conclude that early involvement of critical stakeholder such as regulatory agencies or patients are critical steps in the implementation and later acceptance of platform trials. Addressing these gaps will be critical for attaining the full potential of platform trials for patients.

Funding

Innovative Medicines Initiative 2 Joint Undertaking with support from the European Union's Horizon 2020 research and innovation programme and EFPIA.

A retrospective analysis of conditional power assumptions in clinical trials with continuous or binary endpoints

BACKGROUND

Adaptive clinical trials may use conditional power (CP) to make decisions at interim analyses, requiring assumptions about the treatment effect for remaining patients. It is critical that these assumptions are understood by those using CP in decision-making, as well as timings of these decisions.

METHODS

Data for 21 outcomes from 14 published clinical trials were made available for re-analysis. CP curves for accruing outcome information were calculated using and compared with a pre-specified objective criteria for original and transformed versions of the trial data using four future treatment effect assumptions: (i) observed current trend, (ii) hypothesised effect, (iii) 80% optimistic confidence limit, (iv) 90% optimistic confidence limit.

RESULTS

The hypothesised effect assumption met objective criteria when the true effect was close to that planned, but not when smaller than planned. The opposite was seen using the current trend assumption. Optimistic confidence limit assumptions appeared to offer a compromise between the two, performing well against objective criteria when the end observed effect was as planned or smaller.

CONCLUSION

The current trend assumption could be the preferable assumption when there is a wish to stop early for futility. Interim analyses could be undertaken as early as 30% of patients have data available. Optimistic confidence limit assumptions should be considered when using CP to make trial decisions, although later interim timings should be considered where logistically feasible.

Adaptive clinical trials and master protocols

Methodologies for randomized, double-blind, placebo-controlled clinical trials continue to develop in concert with evolving scientific and translational knowledge. Adaptive trial designs, in which data generated during the study are used to modify subsequent study activity (i.e., sample sizes, entry criteria, or outcomes), can optimize flexibility and expedite the safety and efficacy assessments for interventions of interest. This chapter will summarize general designs, advantages, and pitfalls associated with adaptive clinical trials and compare their features with those of conventional trial designs. It will also review novel ways for which seamless designs and master protocols may improve trial efficiency while offering interpretable data.

Two-stage designs with small sample sizes

When applying group-sequential designs in clinical trials with normally distributed outcomes, approximate critical values are often applied. Here, normally distributed test statistics are assumed which, however, are in fact t-distributed. For small sample sizes, the approximation may lead to a serious inflation of the type I error rate. Recently, a method for computing the exact critical boundaries assuring type I error rate control was proposed and the critical boundaries for Pocock- and O'Brien-Fleming-like group-sequential designs were provided. For designs with one interim analysis, we present six alternative designs, which also control the type I error rate and in addition allow flexible design modifications. We compare the characteristics of these 6 two-stage designs. It is shown that considerable sample size savings can be achieved by including futility stopping and by optimizing the designs. Therefore, for clinical trials with small sample sizes as, for example, in the area of rare diseases, optimal two-stage designs with futility stopping may be a valuable alternative to classical group-sequential designs.

Landscape of Phase II Trials in Alzheimer's Disease

BACKGROUND

Drug development in Alzheimer's disease (AD) over the past two decades has had high rates of failure. Novel trial designs, such as adaptive designs, have the potential to improve the efficiency of drug development in AD.

OBJECTIVE

To evaluate the design characteristics, temporal trends, and differences in design between sponsor types in phase II trials of investigational agents in AD.

METHODS

Phase I/II, II, and II/III trials for AD with drug or other biological interventions registered from December 1996 to December 2021 in ClinicalTrials.gov were included. Descriptive statistics were used to summarize trial characteristics. Linear, logistic, and multinomial regression models assessed temporal trends and differences between sponsor types in design characteristics.

RESULTS

Of N = 474 trials identified, randomized parallel group design was the most common design (72%). Only 12 trials (2.5%) used an adaptive design; adaptive features included early stopping rules, model-based dose-finding, adaptive treatment arm selection, and response adaptive randomization. The use of non-randomized parallel-group and open-label single arm designs increased over time. No temporal trend in the use of adaptive design was identified. Trials sponsored by industry only were more likely to use a randomized parallel-group design and have a larger estimated sample size than trials with other sponsor types.

CONCLUSION

Our systematic review showed that very few phase II trials in AD used an adaptive trial design. Innovation and implementation of novel trial designs in AD trials can accelerate the drug development process.

Group sequential designs in pragmatic trials: feasibility and assessment of utility using data from a number of recent surgical RCTs

BACKGROUND

Assessing the long term effects of many surgical interventions tested in pragmatic RCTs may require extended periods of participant follow-up to assess effectiveness and use patient-reported outcomes that require large sample sizes. Consequently the RCTs are often perceived as being expensive and time-consuming, particularly if the results show the test intervention is not effective. Adaptive, and particularly group sequential, designs have great potential to improve the efficiency and cost of testing new and existing surgical interventions. As a means to assess the potential utility of group sequential designs, we re-analyse data from a number of recent high-profile RCTs and assess whether using such a design would have caused the trial to stop early.

METHODS

Many pragmatic RCTs monitor participants at a number of occasions (e.g. at 6, 12 and 24 months after surgery) during follow-up as a means to assess recovery and also to keep participants engaged with the trial process. Conventionally one of the outcomes is selected as the primary (final) outcome, for clinical reasons, with others designated as either early or late outcomes. In such settings, novel group sequential designs that use data from not only the final outcome but also from early outcomes at interim analyses can be used to inform stopping decisions. We describe data from seven recent surgical RCTs (WAT, DRAFFT, WOLLF, FASHION, CSAW, FIXDT, TOPKAT), and outline possible group sequential designs that could plausibly have been proposed at the design stage. We then simulate how these group sequential designs could have proceeded, by using the observed data and dates to replicate how information could have accumulated and decisions been made for each RCT.

RESULTS

The results of the simulated group sequential designs showed that for two of the RCTs it was highly likely that they would have stopped for futility at interim analyses, potentially saving considerable time (15 and 23 months) and costs and avoiding patients being exposed to interventions that were either ineffective or no better than standard care. We discuss the characteristics of RCTs that are important in order to use the methodology we describe, particularly the value of early outcomes and the window of opportunity when early stopping decisions can be made and how it is related to the length of recruitment period and follow-up.

CONCLUSIONS

The results for five of the RCTs tested showed that group sequential designs using early outcome data would have been feasible and likely to provide designs that were at least as efficient, and possibly more efficient, than the original fixed sample size designs. In general, the amount of information provided by the early outcomes was surprisingly large, due to the strength of correlations with the primary outcome. This suggests that the methods described here are likely to provide benefits more generally across the range of surgical trials and more widely in other application areas where trial designs, outcomes and follow-up patterns are structured and behave similarly.

Practical guidance for planning resources required to support publicly-funded adaptive clinical trials

Adaptive designs are a class of methods for improving efficiency and patient benefit of clinical trials. Although their use has increased in recent years, research suggests they are not used in many situations where they have potential to bring benefit. One barrier to their more widespread use is a lack of understanding about how the choice to use an adaptive design, rather than a traditional design, affects resources (staff and non-staff) required to set-up, conduct and report a trial. The Costing Adaptive Trials project investigated this issue using quantitative and qualitative research amongst UK Clinical Trials Units. Here, we present guidance that is informed by our research, on considering the appropriate resourcing of adaptive trials. We outline a five-step process to estimate the resources required and provide an accompanying costing tool. The process involves understanding the tasks required to undertake a trial, and how the adaptive design affects them. We identify barriers in the publicly funded landscape and provide recommendations to trial funders that would address them. Although our guidance and recommendations are most relevant to UK non-commercial trials, many aspects are relevant more widely.

Most Powerful Test Sequences with Early Stopping Options

We extended the application of uniformly most powerful tests to sequential tests with different stage-specific sample sizes and critical regions. In the one parameter exponential family, likelihood ratio sequential tests are shown to be uniformly most powerful for any predetermined α-spending function and stage-specific sample sizes. To obtain this result, the probability measure of a group sequential design is constructed with support for all possible outcome events, as is useful for designing an experiment prior to having data. This construction identifies impossible events that are not part of the support. The overall probability distribution is dissected into components determined by the stopping stage. These components are the sub-densities of interim test statistics first described by Armitage, McPherson and Rowe (1969) that are commonly used to create stopping boundaries given an α-spending function and a set of interim analysis times. Likelihood expressions conditional on reaching a stage are given to connect pieces of the probability anatomy together. The reduction of support caused by the adoption of an early stopping rule induces sequential truncation (not nesting) in the probability distributions of possible events. Multiple testing induces mixtures on the adapted support. Even asymptotic distributions of inferential statistics that are typically normal, are not. Rather they are derived from mixtures of truncated multivariate normal distributions.

Costs and staffing resource requirements for adaptive clinical trials: quantitative and qualitative results from the Costing Adaptive Trials project

BACKGROUND

Adaptive designs offer great promise in improving the efficiency and patient-benefit of clinical trials. An important barrier to further increased use is a lack of understanding about which additional resources are required to conduct a high-quality adaptive clinical trial, compared to a traditional fixed design. The Costing Adaptive Trials (CAT) project investigated which additional resources may be required to support adaptive trials.

METHODS

We conducted a mock costing exercise amongst seven Clinical Trials Units (CTUs) in the UK. Five scenarios were developed, derived from funded clinical trials, where a non-adaptive version and an adaptive version were described. Each scenario represented a different type of adaptive design. CTU staff were asked to provide the costs and staff time they estimated would be needed to support the trial, categorised into specified areas (e.g. statistics, data management, trial management). This was calculated separately for the non-adaptive and adaptive version of the trial, allowing paired comparisons. Interviews with 10 CTU staff who had completed the costing exercise were conducted by qualitative researchers to explore reasons for similarities and differences.

RESULTS

Estimated resources associated with conducting an adaptive trial were always (moderately) higher than for the non-adaptive equivalent. The median increase was between 2 and 4% for all scenarios, except for sample size re-estimation which was 26.5% (as the adaptive design could lead to a lengthened study period). The highest increase was for statistical staff, with lower increases for data management and trial management staff. The percentage increase in resources varied across different CTUs. The interviews identified possible explanations for differences, including (1) experience in adaptive trials, (2) the complexity of the non-adaptive and adaptive design, and (3) the extent of non-trial specific core infrastructure funding the CTU had.

CONCLUSIONS

This work sheds light on additional resources required to adequately support a high-quality adaptive trial. The percentage increase in costs for supporting an adaptive trial was generally modest and should not be a barrier to adaptive designs being cost-effective to use in practice. Informed by the results of this research, guidance for investigators and funders will be developed on appropriately resourcing adaptive trials.

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.

ANALYSIS OF "LEARN-AS-YOU-GO" (LAGO) STUDIES

In Learn-As-you-GO (LAGO) adaptive studies, the intervention is a complex multicomponent package, and is adapted in stages during the study based on past outcome data. This design formalizes standard practice in public health intervention studies. An effective intervention package is sought, while minimizing intervention package cost. In LAGO study data, the interventions in later stages depend upon the outcomes in the previous stages, violating standard statistical theory. We develop an estimator for the intervention effects, and prove consistency and asymptotic normality using a novel coupling argument, ensuring the validity of the test for the hypothesis of no overall intervention effect. We develop a confidence set for the optimal intervention package and confidence bands for the success probabilities under alternative package compositions. We illustrate our methods in the BetterBirth Study, which aimed to improve maternal and neonatal outcomes among 157,689 births in Uttar Pradesh, India through a multicomponent intervention package.

Advancing Effective Clinical Trial Designs for Myelofibrosis

Design features of phase I, II, and III clinical trials of pharmaceutical interventions in myelofibrosis (MF) are discussed. Model-assisted and model-based designs for phase I trials are useful for maximizing therapeutic benefit and include novel approaches to dose escalation. Trials in MF have shifted to accommodate new challenges following approval of JAK inhibitor therapies. Standardized response criteria exist; however, alternative measures of response when evaluating newer agents may be needed. Noninferiority and other adaptive designs can be used to incorporate design changes over time. Patient-reported outcomes, including quality-of-life and symptom assessment, should be included as outcome measures.

Stakeholder perspectives on adaptive clinical trials: a scoping review

Background

Adaptive clinical trials (ACTs) represent an emerging approach to trial design where accumulating data are used to make decisions about future conduct. Adaptations can include comparisons of multiple dose tiers, response-adaptive randomization, sample size re-estimation, and efficacy/futility stopping rules. The objective of this scoping review is to assess stakeholder attitudes, perspectives, and understanding of adaptive trials.

Methods

We conducted a review of articles examining stakeholders encompassing the broad medical trial community’s perspectives of adaptive designs (ADs). A computerized search was conducted of four electronic databases with relevant search terms. Following screening of articles, the primary findings of each included article were coded for study design, population studied, purpose, and primary implications.

Results

Our team retrieved 167 peer-reviewed titles in total from the database search and 5 additional titles through searching web-based search engines for gray literature. Of those 172 titles, 152 were non-duplicate citations. Of these, 119 were not given full-text reviews, as their titles and abstracts indicated that they did not meet the inclusion criteria. Thirty-three articles were carefully examined for relevance, and of those, 18 were chosen to be part of the analysis; the other 15 were excluded, as they were not relevant upon closer inspection.

Perceived advantages to ADs included limiting ineffective treatments and efficiency in answering the research question; −perceived barriers included insufficient sample size for secondary outcomes, challenges of consent, potential for bias, risk of type 1 error, cost and time to adaptively design trials, unclear rationales for using Ads, and, most importantly, a lack of education regarding ADs among stakeholders within the clinical trial community. Perceptions among different types of stakeholders varied from sector to sector, with patient perspectives being noticeably absent from the literature.

Conclusion

There are diverse perceptions regarding ADs among stakeholders. Further training, guidelines, and toolkits on the proper use of ADs are needed at all levels to overcome many of these perceived barriers. While education for principal investigators is important, it is also crucial to educate other groups in the community, such as patients, as well as clinicians and staff involved in their daily implementation.

Bayesian Semi-parametric Design (BSD) for adaptive dose-finding with multiple strata

In the era of precision medicine, it is of increasing interest to consider multiple strata (e.g. indications, regions, or subgroups) within a single oncology dose-finding study when identifying the maximum tolerated dose (MTD). We propose two Bayesian semi-parametric designs (BSD) for dose-finding with multiple strata to allow for both adaptively dosing patients based on various toxicity profiles and efficient identification of the MTD for each stratum. We develop non-parametric priors based on the Dirichlet process to allow for a flexible prior distribution and negate the need for a pre-specified exchangeability parameter. The two BSD models are built under different prior beliefs of strata heterogeneity and allow for appropriate borrowing of information across similar strata. Simulation studies are performed to evaluate the BSD model performance by comparing it with existing methods, including the fully stratified, exchangeability, and exchangeability-non-exchangeability models. In general, our BSD models outperform the competing methods in correctly identifying the MTD for different strata and necessitate a smaller sample size to determine the MTD. The BSD models are robust to various heterogeneity assumptions and can be easily extended to other binary and time to event endpoints.

On the relative efficiency of model-assisted designs: a conditional approach

In phase I dose-finding trials, model-assisted designs are a novel class of designs that combine the simplicity of algorithm-based methods with the superior performance of model-based methods. Examples of model-assisted designs include the modified toxicity probability (mTPI), Bayesian optimal interval (BOIN) and keyboard designs. To achieve simplicity, these model-assisted methods model only "local" data observed at the current dose, typically using a binomial model, to guide dose assignments. This potentially causes efficiency loss, however, by ignoring the data observed in other doses. To investigate this issue, we propose a conditional approach that utilizes the data from both current and adjacent (i.e., next higher or lower) doses to make the dose-assignment decisions. Specifically, we propose the conditional optimal interval (COIN) design, as the conditional approach extension of the BOIN design. We investigate the theoretical properties of the COIN design and conduct extensive numerical studies to examine its performance in comparison with existing model-assisted designs. We also present the conditional approach to the keyboard design. We observe that the conditional approach improves patient allocation, but yields similar maximum-tolerated dose (MTD) identification accuracy as the model-assisted designs, suggesting only minor efficiency loss using local data under the model-assisted designs.

A Bayesian model-free approach to combination therapy phase I trials using censored time-to-toxicity data

The product of independent beta probabilities escalation (PIPE) design for dual-agent phase I dose-escalation trials is a Bayesian model-free approach for identifying multiple maximum tolerated dose combinations of novel combination therapies. Despite only being published in 2015, the PIPE design has been implemented in at least two oncology trials. However, these trials require patients to have completed follow-up before clinicians can make dose-escalation decisions. For trials of radiotherapy or advanced therapeutics, this may lead to impractically long trial durations due to late-onset treatment-related toxicities. In this paper, we extend the PIPE design to use censored time-to-event (TITE) toxicity outcomes for making dose escalation decisions. We show via comprehensive simulation studies and sensitivity analyses that trial duration can be reduced by up to 35%, particularly when recruitment is faster than expected, without compromising on other operating characteristics.

How to design a dose-finding study using the continual reassessment method

INTRODUCTION

The continual reassessment method (CRM) is a model-based design for phase I trials, which aims to find the maximum tolerated dose (MTD) of a new therapy. The CRM has been shown to be more accurate in targeting the MTD than traditional rule-based approaches such as the 3 + 3 design, which is used in most phase I trials. Furthermore, the CRM has been shown to assign more trial participants at or close to the MTD than the 3 + 3 design. However, the CRM's uptake in clinical research has been incredibly slow, putting trial participants, drug development and patients at risk. Barriers to increasing the use of the CRM have been identified, most notably a lack of knowledge amongst clinicians and statisticians on how to apply new designs in practice. No recent tutorial, guidelines, or recommendations for clinicians on conducting dose-finding studies using the CRM are available. Furthermore, practical resources to support clinicians considering the CRM for their trials are scarce.

METHODS

To help overcome these barriers, we present a structured framework for designing a dose-finding study using the CRM. We give recommendations for key design parameters and advise on conducting pre-trial simulation work to tailor the design to a specific trial. We provide practical tools to support clinicians and statisticians, including software recommendations, and template text and tables that can be edited and inserted into a trial protocol. We also give guidance on how to conduct and report dose-finding studies using the CRM.

RESULTS

An initial set of design recommendations are provided to kick-start the design process. To complement these and the additional resources, we describe two published dose-finding trials that used the CRM. We discuss their designs, how they were conducted and analysed, and compare them to what would have happened under a 3 + 3 design.

CONCLUSIONS

The framework and resources we provide are aimed at clinicians and statisticians new to the CRM design. Provision of key resources in this contemporary guidance paper will hopefully improve the uptake of the CRM in phase I dose-finding trials.

Interval and point estimation in adaptive Phase II trials with binary endpoint

Phase II clinical trials are concerned with making decision of whether a treatment is sufficiently efficacious to be worth further investigations in late large scale Phase III trials. In oncology Phase II trials, frequentist single-arm two-stage group-sequential designs with a binary endpoint are commonly used. To allow for more flexibility, adaptive versions of these designs have been proposed. In this paper, we propose point and interval estimation for adaptive designs in which the second stage sample size is a pre-specified function of first stage's number of responses. Our approach is based on sample space orderings, from which we derive p-values, and point and interval estimates. Simulation studies show that our proposed methods perform better, in terms of bias and root mean square error, than the fixed-sample maximum likelihood estimator.

Statistical advances in clinical trials and clinical research

Introduction

New treatments for neurodegenerative disease are urgently needed, and clinical trial methods are an essential component of new drug development. Although a parallel-group study design for neurological disorder clinical trials is commonly used to test the effectiveness of a new treatment as compared to placebo, it does not efficiently use information from the on-going study to increase the success rate of a trial or to stop a trial earlier when the new treatment is indeed ineffective.

Methods

We review some recent advances in designs for clinical trials, including futility designs and adaptive designs.

Results

Futility designs and noninferiority designs are used to test the nonsuperiority and the noninferiority of a new treatment, respectively. We provide some guidance on using these two designs and analyzing data from these studies properly. Adaptive designs are increasingly used in clinical trials to improve the flexibility and efficiency of trials with the potential to reduce resources, time, and costs. We review some typical adaptive designs and new statistical methods to handle the statistical challenges from adaptive designs.

Discussion

Statistical advances in clinical trial designs may be helpful to shorten study length and benefit more patients being treated with a better treatment during the discovery of new therapies for neurological disorders. Advancing statistical underpinnings of neuroscience research is a critical aspect of the core activities supported by the Center of Biomedical Research Excellence award supporting the Center for Neurodegeneration and Translational Neuroscience.

Mixture-based gatekeeping procedures in adaptive clinical trials

Clinical trials with data-driven decision rules often pursue multiple clinical objectives such as the evaluation of several endpoints or several doses of an experimental treatment. These complex analysis strategies give rise to "multivariate" multiplicity problems with several components or sources of multiplicity. A general framework for defining gatekeeping procedures in clinical trials with adaptive multistage designs is proposed in this paper. The mixture method is applied to build a gatekeeping procedure at each stage and inferences at each decision point (interim or final analysis) are performed using the combination function approach. An advantage of utilizing the mixture method is that it enables powerful gatekeeping procedures applicable to a broad class of settings with complex logical relationships among the hypotheses of interest. Further, the combination function approach supports flexible data-driven decisions such as a decision to increase the sample size or remove a treatment arm. The paper concludes with a clinical trial example that illustrates the methodology by applying it to develop an adaptive two-stage design with a mixture-based gatekeeping procedure.

Timing of the interim analysis in adaptive enrichment designs

With increasing interest in personalized medicine over the last years, study designs allowing to demonstrate efficacy in particular subgroups of the overall patient population become more important. Adaptive enrichment designs provide the possibility to both selecting the target population with the most promising treatment benefit and testing for efficacy within a single trial. Here, the target population is selected in a prespecified interim analysis. So far, it has not been very well investigated how timing of the interim analysis should be chosen. We investigate the impact of the interim analysis timing on power for the situation of a normally distributed outcome considering two different classes of selection rules. The interim selection is based either on the estimated effect difference between subgroup and total population or on absolute effect estimates. In this article, we demonstrate that there are indeed scenarios in which the timing of the interim analysis has a large impact on power. However, no universally applicable timing with favorable performance exist since power depends on treatment effects, subgroup prevalence, and especially the applied selection rule. Instead, the operating characteristics should be investigated for the specific scenario at hand to determine the most appropriate timing.

A 2-in-1 adaptive phase 2/3 design for expedited oncology drug development

We propose an adaptive design that allows us to expand an ongoing Phase 2 trial into a Phase 3 trial to expedite a drug development program with fewer patients. Rather than the usual practice of increasing sample size with a less positive interim outcome, here we propose maintaining sample size with such a result and wait for fully mature data. The final Phase 2 data may be negative, may warrant a larger Phase 3 trial, or, in the extreme, could provide a definitively positive outcome. If the interim outcome is more positive, the trial continues to an originally planned larger sample size for a definitive Phase 3 evaluation. All patients from the study are used for inference regardless of the interim expansion decision. We show that no penalty needs to be paid in order to control the overall Type I error of the study, under a mild assumption that is expected to generally hold in practice. The proposed design may be considered an alternative approach to sample size adjustment for ongoing trials. As such, the use of an intermediate endpoint for adaptive decision is a unique feature of the design. A hypothetical example is provided for illustration purpose.

Covariate-adjusted response-adaptive randomization for multi-arm clinical trials using a modified forward looking Gittins index rule

We introduce a non-myopic, covariate-adjusted response adaptive (CARA) allocation design for multi-armed clinical trials. The allocation scheme is a computationally tractable procedure based on the Gittins index solution to the classic multi-armed bandit problem and extends the procedure recently proposed in Villar et al. (2015). Our proposed CARA randomization procedure is defined by reformulating the bandit problem with covariates into a classic bandit problem in which there are multiple combination arms, considering every arm per each covariate category as a distinct treatment arm. We then apply a heuristically modified Gittins index rule to solve the problem and define allocation probabilities from the resulting solution. We report the efficiency, balance, and ethical performance of our approach compared to existing CARA methods using a recently published clinical trial as motivation. The net savings in terms of expected number of treatment failures is considerably larger and probably enough to make this design attractive for certain studies where known covariates are expected to be important, stratification is not desired, treatment failures have a high ethical cost, and the disease under study is rare. In a two-armed context, this patient benefit advantage comes at the expense of increased variability in the allocation proportions and a reduction in statistical power. However, in a multi-armed context, simple modifications of the proposed CARA rule can be incorporated so that an ethical advantage can be offered without sacrificing power in comparison with balanced designs.

Design and monitoring of multi-arm multi-stage clinical trials

Two-arm group sequential designs have been widely used for over 40 years, especially for studies with mortality endpoints. The natural generalization of such designs to trials with multiple treatment arms and a common control (MAMS designs) has, however, been implemented rarely. While the statistical methodology for this extension is clear, the main limitation has been an efficient way to perform the computations. Past efforts were hampered by algorithms that were computationally explosive. With the increasing interest in adaptive designs, platform designs, and other innovative designs that involve multiple comparisons over multiple stages, the importance of MAMS designs is growing rapidly. This article provides break-through algorithms that can compute MAMS boundaries rapidly thereby making such designs practical. For designs with efficacy-only boundaries the computational effort increases linearly with number of arms and number of stages. For designs with both efficacy and futility boundaries the computational effort doubles with successive increases in number of stages.

Statistical innovations in the medical device world sparked by the FDA

The world of medical devices while highly diverse is extremely innovative, and this facilitates the adoption of innovative statistical techniques. Statisticians in the Center for Devices and Radiological Health (CDRH) at the Food and Drug Administration (FDA) have provided leadership in implementing statistical innovations. The innovations discussed include: the incorporation of Bayesian methods in clinical trials, adaptive designs, the use and development of propensity score methodology in the design and analysis of non-randomized observational studies, the use of tipping-point analysis for missing data, techniques for diagnostic test evaluation, bridging studies for companion diagnostic tests, quantitative benefit-risk decisions, and patient preference studies.

Making what's advanced today routine tomorrow

Statistical principles and ongoing proliferation of novel statistical methodologies have dramatically improved the clinical drug development process. This journey over the last seven decades reshaped the pharmaceutical industry and regulatory agencies, highlighted the importance of statistical thinking in drug development and decision-making, and, most importantly, improved the lives of countless patients around the world. Some significant highlights in the history of this journey are recounted here as well as some exciting opportunities of what the future may hold for the science and profession of statistics.

Incoherent dose-escalation in phase I trials using the escalation with overdose control approach

A desirable property of any dose-escalation strategy for phase I oncology trials is coherence: if the previous patient experienced a toxicity, a higher dose is not recommended for the next patient; similarly, if the previous patient did not experience a toxicity, a lower dose is not recommended for the next patient. The escalation with overdose control (EWOC) approach is a model-based design that has been applied in practice, under which the dose assigned to the next patient is the one that, given all available data, has a posterior probability of exceeding the maximum tolerated dose equal to a pre-specified value known as the feasibility bound. Several methodological and applied publications have considered the EWOC approach with both feasibility bounds fixed and increasing throughout the trial. Whilst the EWOC approach with fixed feasibility bound has been proven to be coherent, some proposed methods of increasing the feasibility bound regardless of toxicity outcomes of patients can lead to incoherent dose-escalation. This paper formalises a proof that incoherent dose-escalation can occur if the feasibility bound is increased without consideration of preceding toxicity outcomes, and shows via simulation studies that only small increases in the feasibility bound are required for incoherent dose-escalations to occur.

A post hoc evaluation of a sample size re-estimation in the Secondary Prevention of Small Subcortical Strokes study

BACKGROUND/AIMS

The use of adaptive designs has been increasing in randomized clinical trials. Sample size re-estimation is a type of adaptation in which nuisance parameters are estimated at an interim point in the trial and the sample size re-computed based on these estimates. The Secondary Prevention of Small Subcortical Strokes study was a randomized clinical trial assessing the impact of single- versus dual-antiplatelet therapy and control of systolic blood pressure to a higher (130-149 mmHg) versus lower (<130 mmHg) target on recurrent stroke risk in a two-by-two factorial design. A sample size re-estimation was performed during the Secondary Prevention of Small Subcortical Strokes study resulting in an increase from the planned sample size of 2500-3020, and we sought to determine the impact of the sample size re-estimation on the study results.

METHODS

We assessed the results of the primary efficacy and safety analyses with the full 3020 patients and compared them to the results that would have been observed had randomization ended with 2500 patients. The primary efficacy outcome considered was recurrent stroke, and the primary safety outcomes were major bleeds and death. We computed incidence rates for the efficacy and safety outcomes and used Cox proportional hazards models to examine the hazard ratios for each of the two treatment interventions (i.e. the antiplatelet and blood pressure interventions).

RESULTS

In the antiplatelet intervention, the hazard ratio was not materially modified by increasing the sample size, nor did the conclusions regarding the efficacy of mono versus dual-therapy change: there was no difference in the effect of dual- versus monotherapy on the risk of recurrent stroke hazard ratios (n = 3020 HR (95% confidence interval): 0.92 (0.72, 1.2), p = 0.48; n = 2500 HR (95% confidence interval): 1.0 (0.78, 1.3), p = 0.85). With respect to the blood pressure intervention, increasing the sample size resulted in less certainty in the results, as the hazard ratio for higher versus lower systolic blood pressure target approached, but did not achieve, statistical significance with the larger sample (n = 3020 HR (95% confidence interval): 0.81 (0.63, 1.0), p = 0.089; n = 2500 HR (95% confidence interval): 0.89 (0.68, 1.17), p = 0.40). The results from the safety analyses were similar to 3020 and 2500 patients for both study interventions. Other trial-related factors, such as contracts, finances, and study management, were impacted as well.

CONCLUSION

Adaptive designs can have benefits in randomized clinical trials, but do not always result in significant findings. The impact of adaptive designs should be measured in terms of both trial results, as well as practical issues related to trial management. More post hoc analyses of study adaptations will lead to better understanding of the balance between the benefits and the costs.

Innovations for phase I dose-finding designs in pediatric oncology clinical trials

Phase I oncology clinical trials are designed to identify the optimal dose that will be recommended for phase II trials. In pediatric oncology, the conduct of those trials raises specific challenges, as the disease is rare with limited therapeutic options. In addition, the tolerance profile is known from adult trials. This paper provides a review of the major recent developments in the design of these trials, inspired by the need to cope with the specific challenges of dose finding in cancer pediatric oncology. We reviewed simulation studies comparing designs dedicated to address these challenges. We also reviewed the design used in published dose-finding trials in pediatric oncology over the period 2009-2014. Three main fields of innovation were identified. First, designs that were developed in order to relax the rules for more flexible inclusions. Second, methods to incorporate data emerging from adult studies. Third, designs accounting for toxicity evaluation at repeated cycles in pediatric oncology. In addition to this overview, we propose some further directions for designing pediatric dose-finding trials.

Implementation of a two-group likelihood time-to-event continual reassessment method using SAS

BACKGROUND AND OBJECTIVES

Dose finding trials using model-based methods have the ability to handle the increasingly complex landscape being seen in clinical trials. Issues such as patient heterogeneity in trial populations are important to address in the designing of a trial in addition to the inclusion/exclusion criteria. Designs accommodating patient heterogeneity have been described using the continual reassessment method (CRM) and time-to-event CRM (TITE-CRM), yet, the implementation of these trials in practice have been limited. These methods and other model-based methods generally need statisticians to help design and conduct these trials. However, the statistical programs which facilitate the use of these methods, currently available focus on estimation in the one-sample case.

METHODS

A SAS program to accommodate two groups using the TITE-CRM and likelihood estimation has been developed. The program consists of macros that assist with the planning and implementation of a trial accounting for patient heterogeneity.

RESULTS

Description of the program is given as well as examples using the programs. For planning purposes, an example will be provided showing how the program can be used to guide sample size estimates for the trial.

CONCLUSIONS

This program provides researchers with a valuable tool for designing dose-finding studies to account for the presence of patient heterogeneity and conduct a trial using a hypothetical example.

Tradeoff-based optimization criteria in clinical trials with multiple objectives and adaptive designs

The article discusses clinical trial optimization problems in the context of mid- to late-stage drug development. Using the Clinical Scenario Evaluation approach, main objectives of clinical trial optimization are formulated, including selection of clinically relevant optimization criteria, identification of sets of optimal and nearly optimal values of the parameters of interest, and sensitivity assessments. The paper focuses on a class of optimization criteria arising in clinical trials with several competing goals, termed tradeoff-based optimization criteria, and discusses key considerations in constructing and applying tradeoff-based criteria. The clinical trial optimization framework considered in the paper is illustrated using two case studies based on a clinical trial with multiple objectives and a two-stage clinical trial which utilizes adaptive decision rules.

Opportunities and challenges of clinical trials in cardiology using composite primary endpoints

In clinical trials, the primary efficacy endpoint often corresponds to a so-called “composite endpoint”. Composite endpoints combine several events of interest within a single outcome variable. Thereby it is intended to enlarge the expected effect size and thereby increase the power of the study. However, composite endpoints also come along with serious challenges and problems. On the one hand, composite endpoints may lead to difficulties during the planning phase of a trial with respect to the sample size calculation, as the expected clinical effect of an intervention on the composite endpoint depends on the effects on its single components and their correlations. This may lead to wrong assumptions on the sample size needed. Too optimistic assumptions on the expected effect may lead to an underpowered of the trial, whereas a too conservatively estimated effect results in an unnecessarily high sample size. On the other hand, the interpretation of composite endpoints may be difficult, as the observed effect of the composite does not necessarily reflect the effects of the single components. Therefore the demonstration of the clinical efficacy of a new intervention by exclusively evaluating the composite endpoint may be misleading. The present paper summarizes results and recommendations of the latest research addressing the above mentioned problems in the planning, analysis and interpretation of clinical trials with composite endpoints, thereby providing a practical guidance for users.

Subgroup-Based Adaptive (SUBA) Designs for Multi-Arm Biomarker Trials

Targeted therapies based on biomarker profiling are becoming a mainstream direction of cancer research and treatment. Depending on the expression of specific prognostic biomarkers, targeted therapies assign different cancer drugs to subgroups of patients even if they are diagnosed with the same type of cancer by traditional means, such as tumor location. For example, Herceptin is only indicated for the subgroup of patients with HER2+ breast cancer, but not other types of breast cancer. However, subgroups like HER2+ breast cancer with effective targeted therapies are rare and most cancer drugs are still being applied to large patient populations that include many patients who might not respond or benefit. Also, the response to targeted agents in humans is usually unpredictable. To address these issues, we propose SUBA, subgroup-based adaptive designs that simultaneously search for prognostic subgroups and allocate patients adaptively to the best subgroup-specific treatments throughout the course of the trial. The main features of SUBA include the continuous reclassification of patient subgroups based on a random partition model and the adaptive allocation of patients to the best treatment arm based on posterior predictive probabilities. We compare the SUBA design with three alternative designs including equal randomization, outcome-adaptive randomization and a design based on a probit regression. In simulation studies we find that SUBA compares favorably against the alternatives.

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.

Practical Methods for Bounding Type I Error Rate with an Internal Pilot Design

New analytic forms for distributions at the heart of internal pilot theory solve many problems inherent to current techniques for linear models with Gaussian errors. Internal pilot designs use a fraction of the data to re-estimate the error variance and modify the final sample size. Too small or too large a sample size caused by an incorrect planning variance can be avoided. However, the usual hypothesis test may need adjustment to control the Type I error rate. A bounding test achieves control of Type I error rate while providing most of the advantages of the unadjusted test. Unfortunately, the presence of both a doubly truncated and an untruncated chi-square random variable complicates the theory and computations. An expression for the density of the sum of the two chi-squares gives a simple form for the test statistic density. Examples illustrate that the new results make the bounding test practical by providing very stable, convergent, and much more accurate computations. Furthermore, the new computational methods are effectively never slower and usually much faster. All results apply to any univariate linear model with fixed predictors and Gaussian errors, with the t-test a special case.