On the need to adjust for multiplicity in confirmatory clinical trials with master protocols

Adding experimental treatment arms to multi-arm multi-stage platform trials in progress

Multi-arm multi-stage (MAMS) platform trials efficiently compare several treatments with a common control arm. Crucially MAMS designs allow for adjustment for multiplicity if required. If for example, the active treatment arms in a clinical trial relate to different dose levels or different routes of administration of a drug, the strict control of the family-wise error rate (FWER) is paramount. Suppose a further treatment becomes available, it is desirable to add this to the trial already in progress; to access both the practical and statistical benefits of the MAMS design. In any setting where control of the error rate is required, we must add corresponding hypotheses without compromising the validity of the testing procedure.To strongly control the FWER, MAMS designs use pre-planned decision rules that determine the recruitment of the next stage of the trial based on the available data. The addition of a treatment arm presents an unplanned change to the design that we must account for in the testing procedure. We demonstrate the use of the conditional error approach to add hypotheses to any testing procedure that strongly controls the FWER. We use this framework to add treatments to a MAMS trial in progress. Simulations illustrate the possible characteristics of such procedures.

Adaptive platform trials: the impact of common controls on type one error and power

Platform trials offer a framework to study multiple interventions in one trial with the opportunity of opening and closing arms. The use of common controls can increase efficiency as compared to individual controls. The need for multiplicity adjustment because of common controls is currently a debate among researchers, pharmaceutical companies, and regulators. The impact of common controls on the type one error in a fixed platform trial, i.e. when all treatments start and end recruitment at the same time, has been discussed in the literature before. We complement these findings by investigating the impact of a common control on the type one error and power in a flexible platform trial, i.e. when one arm joins the platform later. We derived the correlation of test statistics to assess the impact of the overlap and compared the results to a trial with individual controls. Furthermore, we evaluate the power, and the impact of multiplicity adjustment on the power in fixed and flexible platform trials. These methodological considerations are complemented by a regulatory guideline review. With multiple arms, the FWER is inflated when no multiplicity adjustment is applied. However, the FWER inflation is smaller with common controls than with individual controls. Even after multiplicity adjustment, a trial with common controls is often beneficial in terms of sample size and power. However, in some cases, the trial with common controls loses the efficiency gain and it might be advisable to run a separate trial rather than joining a platform trial.

Nasal sprays and behavioural interventions compared with usual care for acute respiratory illness in primary care: a randomised, controlled, open-label, parallel-group trial

BACKGROUND

A small amount of evidence suggests that nasal sprays, or physical activity and stress management, could shorten the duration of respiratory infections. This study aimed to assess the effect of nasal sprays or a behavioural intervention promoting physical activity and stress management on respiratory illnesses, compared with usual care.

METHODS

This randomised, controlled, open-label, parallel-group trial was done at 332 general practitioner practices in the UK. Eligible adults (aged ≥18 years) had at least one comorbidity or risk factor increasing their risk of adverse outcomes due to respiratory illness (eg, immune compromise due to serious illness or medication; heart disease; asthma or lung disease; diabetes; mild hepatic impairment; stroke or severe neurological problem; obesity [BMI ≥30 kg/m2]; or age ≥65 years) or at least three self-reported respiratory tract infections in a normal year (ie, any year before the COVID-19 pandemic). Participants were randomly assigned (1:1:1:1) using a computerised system to: usual care (brief advice about managing illness); gel-based spray (two sprays per nostril at the first sign of an infection or after potential exposure to infection, up to 6 times per day); saline spray (two sprays per nostril at the first sign of an infection or after potential exposure to infection, up to 6 times per day); or a brief behavioural intervention in which participants were given access to a website promoting physical activity and stress management. The study was partially masked: neither investigators nor medical staff were aware of treatment allocation, and investigators who did the statistical analysis were unaware of treatment allocation. The sprays were relabelled to maintain participant masking. Outcomes were assessed using data from participants' completed monthly surveys and a survey at 6 months. The primary outcome was total number of days of illness due to self-reported respiratory tract illnesses (coughs, colds, sore throat, sinus or ear infections, influenza, or COVID-19) in the previous 6 months, assessed in the modified intention-to-treat population, which included all randomly assigned participants who had primary outcome data available. Key secondary outcomes were possible harms, including headache or facial pain, and antibiotic use, assessed in all randomly assigned participants. This trial was registered with ISRCTN, 17936080, and is closed to recruitment.

FINDINGS

Between Dec 12, 2020, and April 7, 2023, of 19 475 individuals screened for eligibility, 13 799 participants were randomly assigned to usual care (n=3451), gel-based nasal spray (n=3448), saline nasal spray (n=3450), or the digital intervention promoting physical activity and stress management (n=3450). 11 612 participants had complete data for the primary outcome and were included in the primary outcome analysis (usual care group, n=2983; gel-based spray group, n=2935; saline spray group, n=2967; behavioural website group, n=2727). Compared with participants in the usual care group, who had a mean of 8·2 (SD 16·1) days of illness, the number of days of illness was significantly lower in the gel-based spray group (mean 6·5 days [SD 12·8]; adjusted incidence rate ratio [IRR] 0·82 [99% CI 0·76-0·90]; p<0·0001) and the saline spray group (6·4 days [12·4]; 0·81 [0·74-0·88]; p<0·0001), but not in the group allocated to the behavioural website (7·4 days [14·7]; 0·97 [0·89-1·06]; p=0·46). The most common adverse event was headache or sinus pain in the gel-based group: 123 (4·8%) of 2556 participants in the usual care group; 199 (7·8%) of 2498 participants in the gel-based group (risk ratio 1·61 [95% CI 1·30-1·99]; p<0·0001); 101 (4·5%) of 2377 participants in the saline group (0·81 [0·63-1·05]; p=0·11); and 101 (4·5%) of 2091 participants in the behavioural intervention group (0·95 [0·74-1·22]; p=0·69). Compared with usual care, antibiotic use was lower for all interventions: IRR 0·65 (95% CI 0·50-0·84; p=0·001) for the gel-based spray group; 0·69 (0·45-0·88; p=0·003) for the saline spray group; and 0·74 (0·57-0·94; p=0·02) for the behavioural website group.

INTERPRETATION

Advice to use either nasal spray reduced illness duration and both sprays and the behavioural website reduced antibiotic use. Future research should aim to address the impact of the widespread implementation of these simple interventions.

FUNDING

National Institute for Health and Care Research.

Regulatory Issues of Platform Trials: Learnings from EU-PEARL

Although platform trials have many benefits, the complexity of these designs may result not only in increased methodological but also regulatory and ethical challenges. These aspects were addressed as part of the IMI project EU Patient-Centric Clinical Trial Platforms (EU-PEARL). We reviewed the available guidelines on platform trials in the European Union and the United States. This is supported and complemented by feedback received from regulatory interactions with the European Medicines Agency and the US Food and Drug Administration. Throughout the project we collected the needs of all relevant stakeholders including ethics committees, regulators, and health technology assessment bodies through active dialog and dedicated stakeholder workshops. Furthermore, we focused on methodological aspects and where applicable identified the corresponding guidance. Learnings from the guideline review, regulatory interactions, and workshops are provided. Based on these, a master protocol template was developed. Issues that still need harmonization or clarification in guidelines or where further methodological research is needed are also presented. These include questions around clinical trial submissions in Europe, the need for multiplicity control across the whole master protocol, the use of non-concurrent controls, and the impact of different randomization schemes. Master protocols are an efficient and patient-centered clinical trial design that can expedite drug development. However, they can also introduce additional operational and regulatory complexities. It is important to understand the different requirements of stakeholders upfront and address them in the trial. While relevant guidance is increasing, early dialog with relevant stakeholders can help to further support such designs.

Distributive randomization: a pragmatic fractional factorial design to screen or evaluate multiple simultaneous interventions in a clinical trial

BACKGROUND

In some medical indications, numerous interventions have a weak presumption of efficacy, but a good track record or presumption of safety. This makes it feasible to evaluate them simultaneously. This study evaluates a pragmatic fractional factorial trial design that randomly allocates a pre-specified number of interventions to each participant, and statistically tests main intervention effects. We compare it to factorial trials, parallel-arm trials and multiple head-to-head trials, and derive some good practices for its design and analysis.

METHODS

We simulated various scenarios involving 4 to 20 candidate interventions among which 2 to 8 could be simultaneously allocated. A binary outcome was assumed. One or two interventions were assumed effective, with various interactions (positive, negative, none). Efficient combinatorics algorithms were created. Sample sizes and power were obtained by simulations in which the statistical test was either difference of proportions or multivariate logistic regression Wald test with or without interaction terms for adjustment, with Bonferroni multiplicity-adjusted alpha risk for both. Native R code is provided without need for compiling or packages.

RESULTS

Distributive trials reduce sample sizes 2- to sevenfold compared to parallel arm trials, and increase them 1- to twofold compared to factorial trials, mostly when fewer allocations than for the factorial design are possible. An unexpectedly effective intervention causes small decreases in power (< 10%) if its effect is additive, but large decreases (possibly down to 0) if not, as for factorial designs. These large decreases are prevented by using interaction terms to adjust the analysis, but these additional estimands have a sample size cost and are better pre-specified. The issue can also be managed by adding a true control arm without any intervention.

CONCLUSION

Distributive randomization is a viable design for mass parallel evaluation of interventions in constrained trial populations. It should be introduced first in clinical settings where many undercharacterized interventions are potentially available, such as disease prevention strategies, digital behavioral interventions, dietary supplements for chronic conditions, or emerging diseases. Pre-trial simulations are recommended, for which tools are provided.

Bayesian Methods for Information Borrowing in Basket Trials: An Overview

Basket trials allow simultaneous evaluation of a single therapy across multiple cancer types or subtypes of the same cancer. Since the same treatment is tested across all baskets, it may be desirable to borrow information across them to improve the statistical precision and power in estimating and detecting the treatment effects in different baskets. We review recent developments in Bayesian methods for the design and analysis of basket trials, focusing on the mechanism of information borrowing. We explain the common components of these methods, such as a prior model for the treatment effects that embodies an assumption of exchangeability. We also discuss the distinct features of these methods that lead to different degrees of borrowing. Through simulation studies, we demonstrate the impact of information borrowing on the operating characteristics of these methods and discuss its broader implications for drug development. Examples of basket trials are presented in both phase I and phase II settings.

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.

Unlocking multidimensional cancer therapeutics using geometric data science

Personalised approaches to cancer therapeutics primarily involve identification of patient sub-populations most likely to benefit from targeted drugs. Such a stratification has led to plethora of designs of clinical trials that are often too complex due to the need for incorporating biomarkers and tissue types. Many statistical methods have been developed to address these issues; however, by the time such methodology is available research in cancer has moved on to new challenges and therefore in order to avoid playing catch-up it is necessary to develop new analytic tools alongside. One of the challenges facing cancer therapy is to effectively and appropriately target multiple therapies for sensitive patient population based on a panel of biomarkers across multiple cancer types, and matched future trial designs. We present novel geometric methods (mathematical theory of hypersurfaces) to visualise complex cancer therapeutics data as multidimensional, as well as geometric representation of oncology trial design space in higher dimensions. The hypersurfaces are used to describe master protocols, with application to a specific example of a basket trial design for melanoma, and thus setup a framework for further incorporating multi-omics data as multidimensional therapeutics.

The Trial within Cohorts (TwiCs) study design in oncology: experience and methodological reflections

A Trial within Cohorts (TwiCs) study design is a trial design that uses the infrastructure of an observational cohort study to initiate a randomized trial. Upon cohort enrollment, the participants provide consent for being randomized in future studies without being informed. Once a new treatment is available, eligible cohort participants are randomly assigned to the treatment or standard of care. Patients randomized to the treatment arm are offered the new treatment, which they can choose to refuse. Patients who refuse will receive standard of care instead. Patients randomized to the standard of care arm receive no information about the trial and continue receiving standard of care as part of the cohort study. Standard cohort measures are used for outcome comparisons. The TwiCs study design aims to overcome some issues encountered in standard Randomized Controlled Trials (RCTs). An example of an issue in standard RCTs is the slow patient accrual. A TwiCs study aims to improve this by selecting patients using a cohort and only offering the intervention to patients in the intervention arm. In oncology, the TwiCs study design has gained increasing interest during the last decade. Despite its potential advantages over RCTs, the TwiCs study design has several methodological challenges that need careful consideration when planning a TwiCs study. In this article, we focus on these challenges and reflect on them using experiences from TwiCs studies initiated in oncology. Important methodological challenges that are discussed are the timing of randomization, the issue of non-compliance (refusal) after randomization in the intervention arm, and the definition of the intention-to-treat effect in a TwiCs study and how this effect is related to its counterpart in standard RCTs.

The population-wise error rate for clinical trials with overlapping populations

We introduce a new multiple type I error criterion for clinical trials with multiple, overlapping populations. Such trials are of interest in precision medicine where the goal is to develop treatments that are targeted to specific sub-populations defined by genetic and/or clinical biomarkers. The new criterion is based on the observation that not all type I errors are relevant to all patients in the overall population. If disjoint sub-populations are considered, no multiplicity adjustment appears necessary, since a claim in one sub-population does not affect patients in the other ones. For intersecting sub-populations we suggest to control the average multiple type I error rate, i.e. the probability that a randomly selected patient will be exposed to an inefficient treatment. We call this the population-wise error rate, exemplify it by a number of examples and illustrate how to control it with an adjustment of critical boundaries or adjusted p -values. We furthermore define corresponding simultaneous confidence intervals. We finally illustrate the power gain achieved by passing from family-wise to population-wise error rate control with two simple examples and a recently suggested multiple-testing approach for umbrella trials.

Design and analysis of umbrella trials: Where do we stand?

Background

The efficiencies that master protocol designs can bring to modern drug development have seen their increased utilization in oncology. Growing interest has also resulted in their consideration in non-oncology settings. Umbrella trials are one class of master protocol design that evaluates multiple targeted therapies in a single disease setting. Despite the existence of several reviews of master protocols, the statistical considerations of umbrella trials have received more limited attention.

Methods

We conduct a systematic review of the literature on umbrella trials, examining both the statistical methods that are available for their design and analysis, and also their use in practice. We pay particular attention to considerations for umbrella designs applied outside of oncology.

Findings

We identified 38 umbrella trials. To date, most umbrella trials have been conducted in early phase settings (73.7%, 28/38) and in oncology (92.1%, 35/38). The quality of statistical information available about conducted umbrella trials to date is poor; for example, it was impossible to ascertain how sample size was determined in the majority of trials (55.3%, 21/38). The literature on statistical methods for umbrella trials is currently sparse.

Conclusions

Umbrella trials have potentially great utility to expedite drug development, including outside of oncology. However, to enable lessons to be effectively learned from early use of such designs, there is a need for higher-quality reporting of umbrella trials. Furthermore, if the potential of umbrella trials is to be realized, further methodological research is required.

Decision rules for identifying combination therapies in open-entry, randomized controlled platform trials

Platform trials have become increasingly popular for drug development programs, attracting interest from statisticians, clinicians and regulatory agencies. Many statistical questions related to designing platform trials-such as the impact of decision rules, sharing of information across cohorts, and allocation ratios on operating characteristics and error rates-remain unanswered. In many platform trials, the definition of error rates is not straightforward as classical error rate concepts are not applicable. For an open-entry, exploratory platform trial design comparing combination therapies to the respective monotherapies and standard-of-care, we define a set of error rates and operating characteristics and then use these to compare a set of design parameters under a range of simulation assumptions. When setting up the simulations, we aimed for realistic trial trajectories, such that for example, a priori we do not know the exact number of treatments that will be included over time in a specific simulation run as this follows a stochastic mechanism. Our results indicate that the method of data sharing, exact specification of decision rules and a priori assumptions regarding the treatment efficacy all strongly contribute to the operating characteristics of the platform trial. Furthermore, different operating characteristics might be of importance to different stakeholders. Together with the potential flexibility and complexity of a platform trial, which also impact the achieved operating characteristics via, for example, the degree of efficiency of data sharing this implies that utmost care needs to be given to evaluation of different assumptions and design parameters at the design stage.

Multiplicity adjustments in parallel-group multi-arm trials sharing a control group: Clear guidance is needed

Multi-arm, parallel-group clinical trials are an efficient way of testing several new treatments, treatment regimens or doses. However, guidance on the requirement for statistical adjustment to control for multiple comparisons (type I error) using a shared control group is unclear. We argue, based on current evidence, that adjustment is not always necessary in such situations. We propose that adjustment should not be a requirement in multi-arm, parallel-group trials testing distinct treatments and sharing a control group, and we call for clearer guidance from stakeholders, such as regulators and scientific journals, on the appropriate settings for adjustment of multiplicity.

Statistical considerations of phase 3 umbrella trials allowing adding one treatment arm mid-trial

Master protocols, in particular umbrella trials and platform trials, when evaluating multiple experimental treatments with a common control, could save patient resource, increase trial efficiency, and reduce drug development cost. Compared to the phase 3 platform trials that allow unlimited number of experimental arms to be added, it is more practical for individual companies to evaluate two experimental arms with a common control in an umbrella trial and allow the second experimental arm to be added at a later time. There have been limited research done in this type of trials in terms of statistical properties and guidance. In this article, we present statistical considerations of a phase 3 three-arm umbrella design including Type I error control and power, as well as the optimal allocation ratio. We intend to not only complement the existing literature, but more importantly to provide practical guidance to pave the way for its implementation by individual companies.

Optimizing subgroup selection in two-stage adaptive enrichment and umbrella designs

We design two‐stage confirmatory clinical trials that use adaptation to find the subgroup of patients who will benefit from a new treatment, testing for a treatment effect in each of two disjoint subgroups. Our proposal allows aspects of the trial, such as recruitment probabilities of each group, to be altered at an interim analysis. We use the conditional error rate approach to implement these adaptations with protection of overall error rates. Applying a Bayesian decision‐theoretic framework, we optimize design parameters by maximizing a utility function that takes the population prevalence of the subgroups into account. We show results for traditional trials with familywise error rate control (using a closed testing procedure) as well as for umbrella trials in which only the per‐comparison type 1 error rate is controlled. We present numerical examples to illustrate the optimization process and the effectiveness of the proposed designs.

Systematic review of available software for multi-arm multi-stage and platform clinical trial design

BACKGROUND

In recent years, the popularity of multi-arm multi-stage, seamless adaptive, and platform trials has increased. However, many design-related questions and questions regarding which operating characteristics should be evaluated to determine the potential performance of a specific trial design remain and are often further complicated by the complexity of such trial designs.

METHODS

A systematic search was conducted to review existing software for the design of platform trials, whereby multi-arm multi-stage trials were also included. The results of this search are reported both on the literature level and the software level, highlighting the software judged to be particularly useful.

RESULTS

In recent years, many highly specialized software packages targeting single design elements on platform studies have been released. Only a few of the developed software packages provide extensive design flexibility, at the cost of limited access due to being commercial or not being usable as out-of-the-box solutions.

CONCLUSIONS

We believe that both an open-source modular software similar to OCTOPUS and a collaborative effort will be necessary to create software that takes advantage of and investigates the impact of all the flexibility that platform trials potentially provide.

Systemic Inflammatory Index Is a Novel Predictor of Intubation Requirement and Mortality after SARS-CoV-2 Infection

Coronavirus disease 2019 (COVID-19), with an increasing number of deaths worldwide, has created a tragic global health and economic emergency. The disease, caused by severe acute respiratory syndrome coronavirus 2019 (SARS-CoV-19), is a multi-system inflammatory disease with many of COVID-19-positive patients requiring intensive medical care due to multi-organ failures. Biomarkers to reliably predict the patient's clinical cause of the virus infection, ideally, to be applied in point of care testing or through routine diagnostic approaches, are highly needed. We aimed to probe if routinely assessed clinical lab values can predict the severity of the COVID-19 course. Therefore, we have retrospectively analyzed on admission laboratory findings in 224 consecutive patients from four hospitals and show that systemic immune inflammation index (SII) is a potent marker for predicting the requirement for invasive ventilator support and for worse clinical outcome of the infected patient. Patients' survival and severity of SARS-CoV-2 infection could reliably be predicted at admission by calculating the systemic inflammatory index of individual blood values. We advocate this approach to be a feasible and easy-to-implement assay that may be particularly useful to improve patient management during high influx crisis. We believe with this work to contribute to improving infrastructure availability and case management associated with COVID-19 pandemic hurdles.

Controlling type I error rates in multi-arm clinical trials: A case for the false discovery rate

Multi-arm trials are an efficient way of simultaneously testing several experimental treatments against a shared control group. As well as reducing the sample size required compared to running each trial separately, they have important administrative and logistical advantages. There has been debate over whether multi-arm trials should correct for the fact that multiple null hypotheses are tested within the same experiment. Previous opinions have ranged from no correction is required, to a stringent correction (controlling the probability of making at least one type I error) being needed, with regulators arguing the latter for confirmatory settings. In this article, we propose that controlling the false-discovery rate (FDR) is a suitable compromise, with an appealing interpretation in multi-arm clinical trials. We investigate the properties of the different correction methods in terms of the positive and negative predictive value (respectively how confident we are that a recommended treatment is effective and that a non-recommended treatment is ineffective). The number of arms and proportion of treatments that are truly effective is varied. Controlling the FDR provides good properties. It retains the high positive predictive value of FWER correction in situations where a low proportion of treatments is effective. It also has a good negative predictive value in situations where a high proportion of treatments is effective. In a multi-arm trial testing distinct treatment arms, we recommend that sponsors and trialists consider use of the FDR.

Multiplicity issues for platform trials with a shared control arm

This paper provides in-depth discussion about different types of error generated in platform trials with a common control arm, and how they compare to the ones arisen from standard independent trials. We provide our views on some of the popular "myths" associated with such design, under the frequentist framework. It is found that platform trial generally performs quite well in terms of type I error rate, false discovery rate, and power. In most cases, these operating characteristics of a platform trial are comparable to or even better than running individual trials.

Efficient Adaptive Designs for Clinical Trials of Interventions for COVID-19

The COVID-19 pandemic has led to an unprecedented response in terms of clinical research activity. An important part of this research has been focused on randomized controlled clinical trials to evaluate potential therapies for COVID-19. The results from this research need to be obtained as rapidly as possible. This presents a number of challenges associated with considerable uncertainty over the natural history of the disease and the number and characteristics of patients affected, and the emergence of new potential therapies. These challenges make adaptive designs for clinical trials a particularly attractive option. Such designs allow a trial to be modified on the basis of interim analysis data or stopped as soon as sufficiently strong evidence has been observed to answer the research question, without compromising the trial's scientific validity or integrity. In this article, we describe some of the adaptive design approaches that are available and discuss particular issues and challenges associated with their use in the pandemic setting. Our discussion is illustrated by details of four ongoing COVID-19 trials that have used adaptive designs.

Non-adjustment for multiple testing in multi-arm trials of distinct treatments: Rationale and justification

There is currently a lack of consensus and uncertainty about whether one should adjust for multiple testing in multi-arm trials of distinct treatments. A detailed rationale is presented to justify non-adjustment in this situation. We argue that non-adjustment should be the default starting position in simple multi-arm trials of distinct treatments.

The Evolution of Master Protocol Clinical Trial Designs: A Systematic Literature Review

PURPOSE

Recent years have seen a change in the way that clinical trials are being conducted. There has been a rise of designs more flexible than traditional adaptive and group sequential trials which allow the investigation of multiple substudies with possibly different objectives, interventions, and subgroups conducted within an overall trial structure, summarized by the term master protocol. This review aims to identify existing master protocol studies and summarize their characteristics. The review also identifies articles relevant to the design of master protocol trials, such as proposed trial designs and related methods.

METHODS

We conducted a comprehensive systematic search to review current literature on master protocol trials from a design and analysis perspective, focusing on platform trials and considering basket and umbrella trials. Articles were included regardless of statistical complexity and classified as reviews related to planned or conducted trials, trial designs, or statistical methods. The results of the literature search are reported, and some features of the identified articles are summarized.

FINDINGS

Most of the trials using master protocols were designed as single-arm (n = 29/50), Phase II trials (n = 32/50) in oncology (n = 42/50) using a binary endpoint (n = 26/50) and frequentist decision rules (n = 37/50). We observed an exponential increase in publications in this domain during the last few years in both planned and conducted trials, as well as relevant methods, which we assume has not yet reached its peak. Although many operational and statistical challenges associated with such trials remain, the general consensus seems to be that master protocols provide potentially enormous advantages in efficiency and flexibility of clinical drug development.

IMPLICATIONS

Master protocol trials and especially platform trials have the potential to revolutionize clinical drug development if the methodologic and operational challenges can be overcome.

Current Statistical Considerations and Regulatory Perspectives on the Planning of Confirmatory Basket, Umbrella, and Platform Trials

Master protocols have received a growing interest during the last years. By assigning patients to specific substudies, they aim at targeting and accelerating clinical development. Given their complexity, basket, umbrella, and platform designs have raised challenging regulatory and statistical questions, especially the control of multiplicity in confirmatory trials. In basket trials, regulatory assessment of the benefit/risk in pooled populations and choice of the treatment indication is challenging. We provide here our perspectives on these topics. In master protocols, as long as the statistical hypotheses tested between the different substudies are independent, no supplementary adjustment for multiplicity over the different substudies should be required. Moreover, sharing a control arm within an umbrella or a platform trial investigating different drugs would not require a correction for the type I error rate, whereas the chance of multiple false positive regulatory decisions should be recognized. In basket trials, pooling across substudies requires a rationale supporting the intended indication and should be preplanned. Assessment of the benefit/risk in pooled target populations can be complicated by differences in design or in efficacy/safety signals between the substudies. While trials governed by a master protocol can offer logistic and financial advantages, more experience is needed to gain a deeper insight into this novel framework.

Basket Designs: Statistical Considerations for Oncology Trials

Progress in the areas of genomics, disease pathways, and drug discovery has advanced into clinical and translational cancer research. The latest innovations in clinical trials have followed with master protocols, which are defined by inclusive eligibility criteria and devised to interrogate multiple therapies for a given tumor histology and/or multiple histologies for a given therapy under one protocol. The use of master protocols for oncology has become more common with the desire to improve the efficiency of clinical research and accelerate overall drug development. Basket trials have been devised to ascertain the extent to which a treatment strategy offers benefit to various patient subpopulations defined by a common molecular target. Conventionally conducted within the phase II setting, basket designs have become popular as drug developers seek to effectively evaluate and identify preliminary efficacy signals among clinical indications identified as promising in preclinical study. This article reviews basket trial designs in oncology settings and discusses several issues that arise with their design and analysis.

Optimized adaptive enrichment designs for three-arm trials: learning which subpopulations benefit from different treatments

We consider the problem of designing a confirmatory randomized trial for comparing two treatments versus a common control in two disjoint subpopulations. The subpopulations could be defined in terms of a biomarker or disease severity measured at baseline. The goal is to determine which treatments benefit which subpopulations. We develop a new class of adaptive enrichment designs tailored to solving this problem. Adaptive enrichment designs involve a preplanned rule for modifying enrollment based on accruing data in an ongoing trial. At the interim analysis after each stage, for each subpopulation, the preplanned rule may decide to stop enrollment or to stop randomizing participants to one or more study arms. The motivation for this adaptive feature is that interim data may indicate that a subpopulation, such as those with lower disease severity at baseline, is unlikely to benefit from a particular treatment while uncertainty remains for the other treatment and/or subpopulation. We optimize these adaptive designs to have the minimum expected sample size under power and Type I error constraints. We compare the performance of the optimized adaptive design versus an optimized nonadaptive (single stage) design. Our approach is demonstrated in simulation studies that mimic features of a completed trial of a medical device for treating heart failure. The optimized adaptive design has $25\%$ smaller expected sample size compared to the optimized nonadaptive design; however, the cost is that the optimized adaptive design has $8\%$ greater maximum sample size. Open-source software that implements the trial design optimization is provided, allowing users to investigate the tradeoffs in using the proposed adaptive versus standard designs.