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

Conditional bias adjusted estimator of treatment effect in 2-in-1 adaptive design

For implementation of adaptive design, the adjustment of bias in treatment effect estimation becomes an increasingly important topic in recent years. While adaptive design literature traditionally focuses on the control of type I error rate and the adjustment of overall unconditional bias, the research on adjusting conditional bias has been limited. This paper proposes a conditional bias adjustment estimator of treatment effect under the context of 2-in-1 adaptive design and aims to provide a comprehensive investigation on their statistical properties including bias, mean squared error and coverage probability of confidence intervals. It demonstrated that conditional bias adjusted estimators greatly reduce the conditional bias and have similarly negligible unconditional bias compared with mean and median (unconditional) unbiased estimators. In addition, the test statistics is constructed based on the conditional bias adjustment estimators and compared with the naive unadjusted test.

Statistical and practical considerations in planning and conduct of dose-optimization trials

The U.S. Food and Drug Administration launched Project Optimus with the aim of shifting the paradigm of dose-finding and selection toward identifying the optimal biological dose that offers the best balance between benefit and risk, rather than the maximum tolerated dose. However, achieving dose optimization is a challenging task that involves a variety of factors and is considerably more complicated than identifying the maximum tolerated dose, both in terms of design and implementation. This article provides a comprehensive review of various design strategies for dose-optimization trials, including phase 1/2 and 2/3 designs, and highlights their respective advantages and disadvantages. In addition, practical considerations for selecting an appropriate design and planning and executing the trial are discussed. The article also presents freely available software tools that can be utilized for designing and implementing dose-optimization trials. The approaches and their implementation are illustrated through real-world examples.

Adaptive two-stage seamless sequential design for clinical trials

We propose an adaptive sequential testing procedure for the selection and testing of multiple treatment options, such as dose/regimen, different drugs, sub-populations, endpoints, or a mixture of them in a seamlessly combined phase II/III trial. The selection is to be made at the end of phase 2 stage. Unlike in many of the published literature, the selection rule is not required to be to "select the best", and does not need to be pre-specified, which provides flexibility and allows the trial investigators to use any efficacy and safety information/criteria, or surrogate or intermediate endpoint to make the selection. Sample size and power calculations are provided. The calculations have been confirmed to be accurate by simulations. Interim analysis can be performed after the selection, sample size can be modified if the observed efficacy deviates from the assumed. Inference after the trial, including p-value, median unbiased point estimate and confidence intervals, are provided. By applying a dominance theorem, the procedure can be applied to normal, binary, Poisson, negative binomial distributed endpoints and time-to-event endpoints, and a mixture of these distributions (in trials involving endpoint selection).

An adaptive seamless 2-in-1 design with biomarker-driven subgroup enrichment

Adaptive seamless phase 2/3 subgroup enrichment design plays a pivotal role in streamlining efficient drug development within a competitive landscape, while also enhancing patient access to promising treatments. This design approach identifies biomarker subgroups with the highest potential to benefit from investigational regimens. The seamless integration of Phase 2 and Phase 3 ensures a timely confirmation of clinical benefits. One significant challenge in adaptive enrichment decisions is determining the optimal timing and maturity of the primary endpoint. In this paper, we propose an adaptive seamless 2-in-1 biomarker-driven subgroup enrichment design that addresses this challenge by allowing subgroup selection using an early intermediate endpoint that predicts clinical benefits (i.e. a surrogate endpoint). The proposed design initiates with a Phase 2 stage involving all participants and can potentially expand into a Phase 3 study focused on the subgroup demonstrating the most favorable clinical outcomes. We will show that, under certain correlation assumptions, the overall type I error may not be inflated at the end of the study. In scenarios where the assumptions may not hold, we present a general framework to control the multiplicity. The flexibility and efficacy of the proposed design are highlighted through an extensive simulation study and illustrated in a case study in multiple myeloma.

A promising biomarker adaptive Phase 2/3 design - Explained and expanded

This short communication concerns a biomarker adaptive Phase 2/3 design for new oncology drugs with an uncertain biomarker effect. Depending on the outcome of an interim analysis for adaptive decision, a Phase 2 study that starts in a biomarker enriched subpopulation may continue to the end without expansion to Phase 3, expand to Phase 3 in the same population or expand to Phase 3 in a broader population. Each path can enjoy full alpha for hypothesis testing without inflating the overall Type I error.

RESTART trial design: two-stage seamless transition design with operational considerations

Oncology/hematology is a competitive therapeutic area where the landscape is constantly evolving. With regulatory support, many drug developers have spent a lot of resources on the operationalization of innovative clinical trial designs, for example, adaptive Bayesian designs in confirmatory clinical trial settings. While overall survival is considered the gold standard in these designs, it is often not a viable choice in identifying treatment efficacy at a reasonable pace, especially for early-stage therapies. In recent years, several binary response surrogate endpoints have been used for accelerated or conditional approval of novel cancer therapies. Utilizing surrogate endpoints in the study design to predict objective clinical outcomes, such as overall survival, is particularly fundamental in cancer treatment clinical development. This manuscript will investigate logistic and statistical considerations of our proposed RESTART design, a new two-stage, seamless, single- to double-arm Bayesian design. This design could be used for single-arm dose expansion to a randomized confirmatory study. The operating characteristics of the RESTART design are evaluated based on simulations. Future directions and further modifications of this design will also be elaborated.

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.

To go or not to go in phase 2/3 oncology trials, a critical question with a unified answer

The decision on how fast to go from single-arm Phase 1 after efficacy single-detection to randomized-controlled Phase 2/3 in oncology drug development is multifaceted and complex, if not often contentious. To facilitate the process, we have incorporated all viable options including the under-utilized adaptive Phase 2/3 designs into a comprehensive decision matrix for completeness and transparency. Illustrated with the 2-in-1 adaptive Phase 2/3 design, a formal decision analysis that explicitly optimizes the tradeoff between cost and benefit reveals a surprisingly robust efficacy bar for Go-No Go transition. It implies that similar robust bars exist for other adaptive Phase 2/3 designs, which remove a major hurdle for their wider adoption in practice.

Adaptive phase 2/3 designs for oncology drug development - Time to hedge

Randomized-controlled Phase 2 trials are routinely skipped in oncology drug development in favor of directly going to confirmatory Phase 3 after signal-detection post dose-finding in Phase 1. With improved standard-of-care, this speed-oriented aggressive approach is not sustainable. It is time to apply the state-of-art adaptive 2-in-1 Phase 2/3 designs to mitigate the risk of costly Phase 3 failures and allow a study to choose the optimal path to success.

Innovations in Clinical Development in Rare Diseases of Children and Adults: Small Populations and/or Small Patients

Many of the afflictions of children are rare diseases. This creates numerous drug development challenges related to small populations, including limited information about the disease state, enrollment challenges, and diminished incentives for pediatric development of novel therapies by pharmaceutical and biotechnology sponsors. We review selected innovations in clinical development that may partially mitigate some of these difficulties, starting with the concept of development efficiency for individual clinical trials, clinical programs (involving multiple trials for a single drug), and clinical portfolios of multiple drugs, and decision analysis as a tool to optimize efficiency. Development efficiency is defined as the ability to reach equally rigorous or more rigorous conclusions in less time, with fewer trial participants, or with fewer resources. We go on to discuss efficient methods for matching targeted therapies to biomarker-defined subgroups, methods for eliminating or reducing the need for natural history data to guide rare disease development, the use of basket trials to enhance efficiency by grouping multiple similar disease applications in a single clinical trial, and the use of alternative data sources including historical controls to augment or replace concurrent controls in clinical studies. Greater understanding and broader application of these methods could lead to improved therapies and/or more widespread and rapid access to novel therapies for rare diseases in both children and adults.

A 2-in-1 adaptive design to seamlessly expand a selected dose from a phase 2 trial to a phase 3 trial for oncology drug development

In oncology, dose-finding studies are largely performed only in Phase I clinical trials and the maximum tolerated dose (MTD), a dose initially developed for systemic chemotherapies, is by default selected for the Phase 3 confirmatory trial. With the advent of anti-cancer therapies such as molecular targeted agents and immunotherapies, a paradigm shift is underway from the use of conventional MTD approaches to improved dose selection strategies for oncology programs. In response to this new challenge, new study designs are needed to optimize dose selection while still bring life-changing new therapies to patients as soon as possible. In this paper, we propose a 2-in-1 adaptive design starting with a Phase 2 trial with randomized evaluation of multiple doses and only select one dose to expand to a Phase 3 trial if efficacy evidence is observed based on an interim evaluation. The lowest dose will be selected if multiple doses show promising efficacy unless the higher dose demonstrates a more compelling treatment effect, and study will be seamlessly expanded to a Phase 3 trial with the selected dose with patients enrolled in the Phase 2 portion also used for the statistical inference in the Phase 3 portion. The overall Type I error can be controlled under a mild assumption. Simulation studies are conducted to confirm the control of Type I error and to demonstrate the desirable operating characteristics of the proposed design.

Family-wise type I error rate control for an extended 2-in-1 design with graphical approach in oncology drug development

Nowadays, in oncology drug development, when an experimental treatment shows a promising anti-tumor effect in Phase I efficacy expansion, a Phase III pivotal trial may be launched directly. To mitigate the risk of skipping the traditional randomized Phase II proof of concept (POC) study, the 2-in-1 design was proposed by Chen et al. (2018). This design has gained great research and application interest since its publication and been extended in many ways. The original 2-in-1 design controls family-wise type I error rate (FWER) for one hypothesis in Phase II part and one hypothesis in Phase III part. However, in practice, for a stand-alone Phase III study usually there are multiple hypotheses with group sequential interim analyses and the multiplicity is controlled by the graphical approach. It is desirable that these features of the Phase III design are retained when 2-in-1 design is considered. The multiplicity control for a 2-in-1 design with multiple hypotheses in Phase III has been addressed mainly by the Bonferroni approach in the literature. For the more powerful graphical approach, while Jin and Zhang (2021) discussed the FWER control for a special 2-in-1 design, in which Phase II and Phase III have exactly the same hypotheses, the FWER control for a more common 2-in-1 design (i.e., one hypothesis in Phase II and multiple hypotheses in Phase III) is yet investigated. This paper provides the analytical conditions under which FWER is controlled with the graphical approach in such a 2-in-1 design. It also provides the numeric explorations of FWER control for such design with group sequential interim analyses in Phase III, as a direct Phase III design normally would have. As a result, our work helps lower the hurdle of the application of the 2-in-1 design and pave the way for its wider application.

An optimal two-stage exploratory basket trial design with aggregated futility analysis

A basket trial investigates the effects of one drug on multiple tumor indications. To discontinue potentially inactive indications early, interim futility analysis is usually conducted for each indication individually once it reaches the pre-specified sample size. As enrollment rates vary among indications, the futility decisions for slow-enrolling indications could be made much later than other fast-enrolling indications, which could delay the overall decision for the trial significantly. To accelerate the futility decision in early-stage exploratory basket trials and potentially reallocate resources to other compounds earlier while still controlling the global type-I and type-II errors, we propose an optimal two-stage basket trial design with one aggregated futility analysis by aggregating (e.g., pooling) all indications together. The total sample size across all indications is pre-specified for the futility analysis, while the sample size per indication can be adapted to the enrollment rate. The final analysis is performed using the pruning and pooling approach (Chen et al. 2016). The design parameters are optimized by minimizing the expected total sample size under the null hypothesis, while explicitly controlling the global type-I and the type-II error rates. Simulation studies demonstrate that the proposed design has better operating characteristics than the designs with individual futility analysis (Zhou et al. 2019; Wu et al. 2021), while allowing for earlier futility decision.

Optimal one-stage design and analysis for efficacy expansion in Phase I oncology trials

BACKGROUND

Contemporary Phase I oncology trials often include efficacy expansion in various tumor indications post dose finding. Preliminary anti-tumor activity from efficacy expansion can aid Go/No-Go decision for Phase 2 or Phase 3 initiation. Tumor cohorts in efficacy expansion are commonly analyzed independently in practice, which are often underpowered due to small sample size. Pooled analysis is also sometimes conducted, but it ignores the heterogeneity of the anti-tumor activity across cohorts.

METHODS

We propose an optimal one-stage design and analysis strategy for the efficacy expansion to assess whether the treatment is effective. Allowing heterogeneous anti-tumor effects across tumor cohorts, inactive cohorts are pruned, and the potentially active cohorts are pooled together to gain study power. For a prospective design with a target power, the total sample size across all cohorts is minimized; or for an ad hoc analysis with pre-specified sample size for each cohort, the pruning criteria are optimized to achieve maximum power. The global type I error is controlled after proper multiplicity adjustment, and a penalty adjusted significance level is used for the pooled test.

RESULTS

Simulation studies show that the proposed optimal design has desirable operating characteristics in increasing the overall power and detecting more true positive tumor cohorts.

CONCLUSION

The proposed optimal design and analysis strategy provides a practical approach to design and analyze heterogeneous efficacy expansion cohorts in a basket setting with global type I and type II error being controlled.

Estimation of treatment effect in 2-in-1 adaptive design and some of its extensions

The 2-in-1 adaptive design allows seamless expansion of an ongoing Phase II trial into a Phase III trial to expedite a drug development program. Since its publication, it has generated a lot of interest. So far, most of the related research focused on type I error control. Similar to most adaptive designs, 2-in-1 design could also pose a great challenge on estimation of treatment effect due to the data-driven adaptation. In addition, the use of intermediate endpoint for interim adaptive decision-making is a less well-studied field. In this paper, we investigate the bias and variances in estimation for 2-in-1 design and some of its extensions, and propose some bias-adjusted estimators for 2-in-1 design. The properties of the proposed estimators are further studied theoretically and/or numerically, so as to provide guidance on how to interpret the estimated treatment effect of 2-in-1 design.

An adaptive seamless Phase 2-3 design with multiple endpoints

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

Bayesian decision making in confirmatory early-stage breast cancer trial

Preoperative or neoadjuvant systemic chemotherapy, once reserved for patients with locally advanced breast cancer (BC) in whom the goal was to render breast cancer operable, has become increasingly common. In the early-stage BC neoadjuvant studies, clinical benefits such as event-free survival (EFS), disease-free survival (DFS) and overall survival (OS) usually take long time to be observed. Pathological complete response (pCR) rate obtained at surgery as an endpoint after the neoadjuvant treatment has been accepted by FDA as a surrogate predictor for long-term time-to-event endpoints to support accelerated approval. Utilizing this early endpoint helps expedite the development of novel therapies in order to fulfill the unmet medical need for certain high-risk or poor prognosis subsets of early-stage BC patients. By applying the correlation between pCR and time-to-event endpoints, an early and informative Go/NoGo decision-making structure can be built with less cost so that it improves the overall clinical development efficiency. We propose a Bayesian hierarchy model procedure that utilizes Bayesian predictive power of EFS in phase III to guide the Go/NoGo decision based on a clinical plausible threshold for the pCR treatment difference in phase II. The model implements a double bootstrap method to estimate the correlation between pCR and EFS in simulated setting. Besides simulation results, a hypothetical example based on the 2-in-1 adaptive design is provided.

A systematic review of the "promising zone" design

Introduction

Sample size calculations require assumptions regarding treatment response and variability. Incorrect assumptions can result in under- or overpowered trials, posing ethical concerns. Sample size re-estimation (SSR) methods investigate the validity of these assumptions and increase the sample size if necessary. The “promising zone” (Mehta and Pocock, Stat Med 30:3267–3284, 2011) concept is appealing to researchers for its design simplicity. However, it is still relatively new in the application and has been a source of controversy.

Objectives

This research aims to synthesise current approaches and practical implementation of the promising zone design.

Methods

This systematic review comprehensively identifies the reporting of methodological research and of clinical trials using promising zone. Databases were searched according to a pre-specified search strategy, and pearl growing techniques implemented.

Results

The combined search methods resulted in 270 unique records identified; 171 were included in the review, of which 30 were trials. The median time to the interim analysis was 60% of the original target sample size (IQR 41–73%). Of the 15 completed trials, 7 increased their sample size. Only 21 studies reported the maximum sample size that would be considered, for which the median increase was 50% (IQR 35–100%).

Conclusions

Promising zone is being implemented in a range of trials worldwide, albeit in low numbers. Identifying trials using promising zone was difficult due to the lack of reporting of SSR methodology. Even when SSR methodology was reported, some had key interim analysis details missing, and only eight papers provided promising zone ranges.

Sample size re-estimation for pivotal clinical trials

It is well known that if the hypothesis test is left unchanged, the Type I error rate may be inflated for sample size re-estimation (SSR) designs. To address this issue, three main approaches have been proposed in the literature: combination test, conditional error and conventional test with sample size increase in the allowable region (AR) only. These three seemingly different approaches are in fact connected. For each combination test, there is a corresponding conditional error function and AR. Designing adaptation rules in this AR with conventional test guarantees the Type I error rate control but at the same time always leads to smaller power comparing to the corresponding combination test (or conditional error) approach. In cases where conventional test is still preferable, step-wise type adaptation rules that do not fully reside in the AR can be alternatively considered. We believe controversies in the statistical community on the efficiency comparisons between group sequential (GS) and SSR design stem partially from the misalignment of performance metrics and conditional versus unconditional evaluations. We advocate summary metrics, such as median, variance or tail probabilities of the sample size in addition to expectation and personalizing efficiency definition for each trial sponsor. Conditional metrics by favorable, promising and unfavorable zones of the interim results provide additional insights and should always be incorporated into the decision-making process.

Short Time to Market and Forward Planning Will Enable Cell Therapies to Deliver R&D Pipeline Value

There is considerable industry excitement about the curative potential of cell and gene therapies, but significant challenges remain in designing cost-effective treatments that are accessible globally. We have taken a modeling-based approach to define the cost and value drivers for cell therapy assets during pharmaceutical drug development. We have created a model development program for a lentiviral modified ex vivo autologous T cell therapy for Oncology indications. Using internal and external benchmarks, we have estimated the total out-of-pocket cost of development for an Oncology cell therapy asset from target identification to filing of marketing application to be $500-600 million. Our model indicates that both clinical and Chemistry Manufacturing and Controls (CMC) cost of development for cell therapies are higher due to unique considerations of ex vivo autologous cell therapies. We have computed a threshold revenue-generating patient number for our model asset that enables selection of assets that can address high unmet medical need and generate pipeline value. Using statistical approaches, we identified that short time to market (<5 years) and reduced commercial cost of goods (<$65,000 per dose) will be essential in developing competitive assets and we propose solutions to reduce both. We emphasize that teams must proactively plan alternate development scenarios with clear articulation of path to value generation and greater patient access. We recommend using a modeling-based approach to enable data driven go/no-go decisions during multigenerational cell therapy development.

The extension of 2-in-1 adaptive phase 2/3 designs and its application in oncology clinical trials

A 2-in-1 adaptive Phase 2/3 design was proposed by Chen et al. The 2-in-1 design improves the overall clinical trial development efficiency by 1) building in an early and informative decision-making; 2) allowing the flexible endpoint-usage at the decision and the final analysis; 3) potential registration path forward in either Phase 2 or Phase 3. The original paper illustrates a general idea. In this paper, we extend this design to fit more common scenarios. The type I error control in the extended 2-in-1 adaptive Phase 2/3 designs is investigated in both simulation and theoretical ways.

Adaptive design implementation in confirmatory trials: methods, practical considerations and case studies

The rapidly changing drug development landscapes have brought unique challenges to sponsors in designing clinical trials in a faster and more efficient way. With the ability to accelerate development timeline, reduce redundant sample size, and select the right dose and patient population during the clinical trial, adaptive designs help to increase the probability of success of clinical trials and eventually contribute to bringing the promising drugs to patients earlier and fulfilling their unmet medical needs. Although extensive adaptive design methods have been proposed in recent years, a comprehensive review of how to implement adaptive design in the practical confirmatory trials is still lacking. In this paper, we will review the evolving history of adaptive designs, updates of newly released regulatory guidance and emerging practical adaptive designs, including but not limited to sample size re-estimation, seamless design and surrogate endpoint used in the interim analysis. Furthermore, we will discuss the current practice of adaptive design implementation by demonstrating a complex oncology seamless phase 2/3 adaptive design case study. Through this example, we will introduce the critical roles of each cross disciplinary function, communication process and important documents when adaptive designs are implemented in real-world setting.

Should Overall Survival Remain an Endpoint for Multiple Myeloma Trials?

PURPOSE OF REVIEW

While the traditional gold standard for demonstrating clinical benefit of a therapy has been to show prolongation of overall survival (OS), there are multiple factors which can hinder the use of OS as a primary endpoint in randomized clinical trials (RCTs). Here, we analyze recent myeloma RCTs and evaluate the issues relevant to current and future myeloma RCT design.

RECENT FINDINGS

A review of recent phase III RCTs that led to approval of new agents/combinations reveals that none were designed with OS as the primary endpoint, but instead utilized time to progression (TTP) or progression-free survival (PFS). These studies illuminate the inherent difficulties of designing trials with the primary endpoint of OS/PFS in a disease characterized by increasingly prolonged survival times, availability of effective salvage therapies, and competing events such as co-morbid conditions. Alternative primary endpoints other than OS or PFS need to be developed for future myeloma RCTs. Validated surrogate endpoints with novel clinical trial designs will help improve the feasibility of conducting comparative clinical trials in a timely manner.

An adaptive population enrichment phase III trial of TRC105 and pazopanib versus pazopanib alone in patients with advanced angiosarcoma (TAPPAS trial)

Background

Major challenges in clinical trials of ultra-orphan oncology diseases include limited patient availability and paucity of reliable prior data for estimating the treatment effect and, therefore, determining optimal sample size. Angiosarcoma (AS), a particularly aggressive form of soft tissue sarcoma with an incidence of about 2000 cases per year in the United States and Europe is poorly addressed by current systemic therapies. Pazopanib, an inhibitor of vascular endothelial growth factor receptor (VEGFR) is approved for the treatment of AS, with modest benefit. TRC105 (carotuximab) is a monoclonal antibody to endoglin, an essential angiogenic target highly expressed on proliferating endothelium and both tumor vessels and tumor cells in AS, that has the potential to complement VEGFR tyrosine kinase inhibitors. In a phase I/II study of soft tissue sarcoma, TRC105 combined safely with pazopanib and the combination demonstrated durable complete responses and encouraging progression-free survival (PFS). In addition, there was a suggestion of superior benefit in patients with cutaneous lesions versus those with the non-cutaneous lesions.

Patients and methods

This article describes the design of a recently initiated phase III trial of TRC105 And Pazopanib versus Pazopanib alone in patients with advanced AngioSarcoma (TAPPAS trial). Given the ultra-orphan status of the disease and the paucity of reliable prior data on PFS or overall survival (end points required for regulatory approval as a pivotal trial), an adaptive design incorporating population enrichment and sample size re-estimation was implemented. The design incorporated regulatory input from the Food and Drug Administration (FDA) and European Medicines Agency and proceeded following special protocol assessment designation by the FDA.

Conclusions

It is shown that the benefit of the adaptive design as compared with a conventional single-look design arises from the learning and subsequent improvements in power that occur after an unblinded analysis of interim data.

Registered on Clinicaltrials.gov

NCT02979899.

Optimal two-stage designs for exploratory basket trials

The primary goal of an exploratory oncology clinical trial is to identify an effective drug for further development. To account for tumor indication selection error, multiple tumor indications are often selected for simultaneous testing in a basket trial. In this article, we propose optimal and minimax two-stage basket trial designs for exploratory clinical trials. Inactive tumor indications are pruned in stage 1 and the active tumor indications are pooled at end of stage 2 to assess overall effectiveness of the test drug. The proposed designs explicitly control the type I and type II error rates with closed-form sample size formula. They can be viewed as a natural extension of Simon's optimal and minimax two-stage designs for single arm trials to multi-arm basket trials. A simulation study shows that the proposed design method has desirable operating characteristics as compared to other commonly used design methods for exploratory basket trials.

The Vitamin C, Thiamine and Steroids in Sepsis (VICTAS) Protocol: a prospective, multi-center, double-blind, adaptive sample size, randomized, placebo-controlled, clinical trial

BACKGROUND

Sepsis accounts for 30% to 50% of all in-hospital deaths in the United States. Other than antibiotics and source control, management strategies are largely supportive with fluid resuscitation and respiratory, renal, and circulatory support. Intravenous vitamin C in conjunction with thiamine and hydrocortisone has recently been suggested to improve outcomes in patients with sepsis in a single-center before-and-after study. However, before this therapeutic strategy is adopted, a rigorous assessment of its efficacy is needed.

METHODS

The Vitamin C, Thiamine and Steroids in Sepsis (VICTAS) trial is a prospective, multi-center, double-blind, adaptive sample size, randomized, placebo-controlled trial. It will enroll patients with sepsis causing respiratory or circulatory compromise or both. Patients will be randomly assigned (1:1) to receive intravenous vitamin C (1.5 g), thiamine (100 mg), and hydrocortisone (50 mg) every 6 h or matching placebos until a total of 16 administrations have been completed or intensive care unit discharge occurs (whichever is first). Patients randomly assigned to the comparator group are permitted to receive open-label stress-dose steroids at the discretion of the treating clinical team. The primary outcome is consecutive days free of ventilator and vasopressor support (VVFDs) in the 30 days following randomization. The key secondary outcome is mortality at 30 days. Sample size will be determined adaptively by using interim analyses with pre-stated stopping rules to allow the early recognition of a large mortality benefit if one exists and to refocus on the more sensitive outcome of VVFDs if an early large mortality benefit is not observed.

DISCUSSION

VICTAS is a large, multi-center, double-blind, adaptive sample size, randomized, placebo-controlled trial that will test the efficacy of vitamin C, thiamine, and hydrocortisone as a combined therapy in patients with respiratory or circulatory dysfunction (or both) resulting from sepsis. Because the components of this therapy are inexpensive and readily available and have very favorable risk profiles, demonstrated efficacy would have immediate implications for the management of sepsis worldwide.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03509350 . First registered on April 26, 2018, and last verified on December 20, 2018. Protocol version: 1.4, January 9, 2019.