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Platform trials (PTs) are gaining popularity in clinical research due to their innovative and flexible methodologies. The objective was to determine the characteristics, methodological, and statistical practices in PTs. We identified PTs from trial registries and bibliographic databases up to August 2024. Eligible PTs were randomized controlled trials studying multiple interventions within a single population, with flexibility to add or drop arms. Data were extracted on trial status, design, statistical methods, and reporting practices. We identified 189 PTs. Most focused on infectious diseases (77, including 57 for COVID-19) and oncology (68). PT initiation peaked during the COVID-19 pandemic but has since stabilized at 84 active PTs, with 25 in planning. A complete master protocol was available for 47% (89/189) of PTs. Bayesian designs featured in 58/189 PTs vs. 56/189 frequentist trials, 20/189 trials utilizing both (unclear in 55/189 PTs). Overall, 25/111 trials (23%) were designed without a predetermined target sample size, all of which were Bayesian. Among these, 16 were explicitly reported as “perpetual” trials. The number of interim analyses was predetermined in 18% (10/57) of Bayesian trials vs. 58% (28/48) of frequentist trials. Simulations to evaluate operating characteristics were used in 93% (39/42) of Bayesian trials. Simulation reports were available in 67% (26/39) of cases, and the procedures were detailed for 62% (24/39) of trials. Only two trials shared the simulation code. PTs remain popular and increasingly diverse. Efforts to enhance transparency and reporting, especially in complex Bayesian PTs, are essential to ensure reliability and broader acceptance. •Bayesian designs were as common as frequentist designs.•Simulations to evaluate operating characteristics were used in 93% of Bayesian trials.•Simulation reports were available in only 67% of cases in Bayesian trials.•Simulation procedures were detailed in only 62% of cases in Bayesian trials.•Efforts are needed to enhance transparency and reporting of platform trial features.

Background Interventional clinical studies conducted in the regulated drug research environment are designed using International Council for Harmonisation (ICH) regulatory guidance documents: ICH E6 (R2) Good Clinical Practice—scientific guideline, first published in 2002 and last updated in 2016. This document provides an international ethical and scientific quality standard for designing and conducting trials that involve the participation of human subjects. Recently, there has been heightened awareness of the importance of integrated research platform trials (IRPs) designed to evaluate multiple therapies simultaneously. The use of a single master protocol as a key source document to fulfill trial conduct obligations has resulted in a re-examination of the templates used to fulfill the dynamic regulatory and modern drug development environment challenges. Methods Regulatory medical writing, biostatistical, and other members of EU Patient-cEntric clinicAl tRial pLatforms (EU-PEARL) developed the suite of templates for IRPs over a 3.5-year period. Stakeholders contributing expertise included academic hospitals, pharmaceutical companies, non-governmental organizations, patient representative groups, and small and medium-sized enterprises (SMEs). Results The suite of templates for IRPs based on TransCelerate’s Common Protocol Template (CPT) and statistical analysis plan (SAP) should help authors navigate relevant guidelines as they create study design content relevant for today’s IRP studies. It offers practical suggestions for adaptive platform designs which offer flexible features such as dropping treatments for futility or adding new treatments to be tested during a trial. The EU-PEARL suite of templates for IRPs comprises a preface, followed by the actual resource. The preface clarifies the intended use and underlying principles that inform resource utility. The preface lists references contributing to the development of the resource. The resource includes TransCelerate CPT guidance text, and EU-PEARL-derived guidance text, distinguished from one another using shading. Rationale comments are used throughout for clarification purposes. In addition, a user-friendly, functional, and informative Platform Trials Best Practices tool to support the setup, design, planning, implementation, and conduct of complex and innovative trials to support multi-sourced/multi-company platform trials is also provided. Together, the EU-PEARL suite of templates and the Platform Trials Best Practices tool constitute the reference user manual. Conclusions This publication is intended to enhance the use, understanding, and dissemination of the EU-PEARL suite of templates for designing IRPs. The reference user manual and the associated website ( http://www.eu-pearl ) should facilitate the designing of IRP trials.

The current recommendation for treating Lassa fever with ribavirin is supported only by weak evidence. Given the persistent effects in areas with endemic transmission and epidemic potential, there is an urgent need to reassess ribavirin and investigate other potential therapeutic candidates; however, a robust clinical trial method adapted to Lassa fever epidemiology has not yet been established. We propose an adaptive phase II/III multicenter randomized controlled platform trial that uses a superiority framework with an equal allocation ratio and accounts for challenges selecting the primary end point and estimating the target sample size by using an interim analysis.

The United States Government (USG) public-private partnership “Accelerating COVID-19 Treatment Interventions and Vaccines” (ACTIV) was launched to identify safe, effective therapeutics to treat patients with Coronavirus Disease 2019 (COVID-19) and prevent hospitalization, progression of disease, and death. Eleven original master protocols were developed by ACTIV, and thirty-seven therapeutic agents entered evaluation for treatment benefit. Challenges encountered during trial implementation led to innovations enabling initiation and enrollment of over 26,000 participants in the trials. While only two ACTIV trials continue to enroll, the recommendations here reflect information from all the trials as of May 2023. We review clinical trial implementation challenges and corresponding lessons learned to inform future therapeutic clinical trials implemented in response to a public health emergency and the conduct of complex clinical trials during “peacetime,” as well.

The Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) Cross-Trial Statistics Group gathered lessons learned from statisticians responsible for the design and analysis of the 11 ACTIV therapeutic master protocols to inform contemporary trial design as well as preparation for a future pandemic. The ACTIV master protocols were designed to rapidly assess what treatments might save lives, keep people out of the hospital, and help them feel better faster. Study teams initially worked without knowledge of the natural history of disease and thus without key information for design decisions. Moreover, the science of platform trial design was in its infancy. Here, we discuss the statistical design choices made and the adaptations forced by the changing pandemic context. Lessons around critical aspects of trial design are summarized, and recommendations are made for the organization of master protocols in the future.

Background Master protocols, classified as basket trials, umbrella trials, and platform trials, are novel designs that investigate multiple hypotheses through concurrent sub-studies (e.g., multiple treatments or populations or that allow adding/removing arms during the trial), offering enhanced efficiency and a more ethical approach to trial evaluation. Despite the many advantages of these designs, they are infrequently used. Methods We conducted a landscape analysis of master protocols using a systematic literature search to determine what trials have been conducted and proposed for an overall goal of improving the literacy in this emerging concept. On July 8, 2019, English-language studies were identified from MEDLINE, EMBASE, and CENTRAL databases and hand searches of published reviews and registries. Results We identified 83 master protocols (49 basket, 18 umbrella, and 16 platform trials). The number of master protocols has increased rapidly over the last five years. Most have been conducted in the US ( n = 44/83) and investigated experimental drugs ( n = 82/83) in the field of oncology ( n = 76/83). The majority of basket trials were exploratory (i.e., phase I/II; n = 47/49) and not randomized ( n = 44/49), and more than half ( n = 28/48) investigated only a single intervention. The median sample size of basket trials was 205 participants (interquartile range, Q3-Q1 [IQR]: 500–90 = 410), and the median study duration was 22.3 (IQR: 74.1–42.9 = 31.1) months. Similar to basket trials, most umbrella trials were exploratory ( n = 16/18), but the use of randomization was more common ( n = 8/18). The median sample size of umbrella trials was 346 participants (IQR: 565–252 = 313), and the median study duration was 60.9 (IQR: 81.3–46.9 = 34.4) months. The median number of interventions investigated in umbrella trials was 5 (IQR: 6–4 = 2). The majority of platform trials were randomized ( n = 15/16), and phase III investigation ( n = 7/15; one did not report information on phase) was more common in platform trials with four of them using seamless II/III design. The median sample size was 892 (IQR: 1835–255 = 1580), and the median study duration was 58.9 (IQR: 101.3–36.9 = 64.4) months. Conclusions We anticipate that the number of master protocols will continue to increase at a rapid pace over the upcoming decades. More efforts to improve awareness and training are needed to apply these innovative trial design methods to fields outside of oncology.

The objective of the study was to outline key considerations for general clinical readers when critically evaluating publications on platform trials and for researchers when designing these types of clinical trials. In this review, we describe key concepts of platform trials with case study discussion of two hallmark platform trials in STAMPEDE and I-SPY2. We provide reader's guide to platform trials with a critical appraisal checklist. Platform trials offer flexibilities of dropping ineffective arms early based on interim data and introducing new arms into the trial. For platform trials, it is important to consider how interventions are compared and evaluated throughout and how new interventions are introduced. For intervention comparisons, it is important to consider what the primary analysis is, what and how many interventions are active simultaneously, and allocation between different arms. Interim evaluation considerations should include the number and timing of interim evaluations and outcomes and statistical rules used to drop interventions. New interventions are usually introduced based on scientific merits, so consideration of these merits is important, together with the timing and mechanisms in which new interventions are added. More efforts are needed to improve the scientific literacy of platform trials. Our review provides an overview of the important concepts of platform trials. •In this review article, we provide reader's guide to platform trials with a critical appraisal checklist.•Platform trials are an extension of adaptive multiarm, multistage trial designs that allow for evaluation of multiple interventions using interim evaluations and addition of new interventions during the trial.•For platform trials, it is important to consider how interventions are compared, how interim evaluations are conducted, and how new interventions are introduced in a given platform trial.•For comparison of interventions, it is important to consider what the primary analysis is, whether the platform trial addresses subgroup effects, the number of interventions that are active at once, and allocation between intervention and control groups.•Interim evaluation considerations should include the frequency, timing, and outcome used for interim evaluations, as well as the statistical rules that are used to drop or graduate interventions onto the next stage.•The scientific merits used to determine what interventions are added into the trial should be considered as well as the timing and how they are added.

Adaptive designs for clinical trials permit alterations to a study in response to accumulating data in order to make trials more flexible, ethical, and efficient. These benefits are achieved while preserving the integrity and validity of the trial, through the pre-specification and proper adjustment for the possible alterations during the course of the trial. Despite much research in the statistical literature highlighting the potential advantages of adaptive designs over traditional fixed designs, the uptake of such methods in clinical research has been slow. One major reason for this is that different adaptations to trial designs, as well as their advantages and limitations, remain unfamiliar to large parts of the clinical community. The aim of this paper is to clarify where adaptive designs can be used to address specific questions of scientific interest; we introduce the main features of adaptive designs and commonly used terminology, highlighting their utility and pitfalls, and illustrate their use through case studies of adaptive trials ranging from early-phase dose escalation to confirmatory phase III studies.

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

Background Platform trials can evaluate the efficacy of several experimental treatments compared to a control. The number of experimental treatments is not fixed, as arms may be added or removed as the trial progresses. Platform trials are more efficient than independent parallel group trials because of using shared control groups. However, for a treatment entering the trial at a later time point, the control group is divided into concurrent controls, consisting of patients randomised to control when that treatment arm is in the platform, and non-concurrent controls, patients randomised before. Using non-concurrent controls in addition to concurrent controls can improve the trial’s efficiency by increasing power and reducing the required sample size, but can introduce bias due to time trends. Methods We focus on a platform trial with two treatment arms and a common control arm. Assuming that the second treatment arm is added at a later time, we assess the robustness of recently proposed model-based approaches to adjust for time trends when utilizing non-concurrent controls. In particular, we consider approaches where time trends are modeled either as linear in time or as a step function, with steps at time points where treatments enter or leave the platform trial. For trials with continuous or binary outcomes, we investigate the type 1 error rate and power of testing the efficacy of the newly added arm, as well as the bias and root mean squared error of treatment effect estimates under a range of scenarios. In addition to scenarios where time trends are equal across arms, we investigate settings with different time trends or time trends that are not additive in the scale of the model. Results A step function model, fitted on data from all treatment arms, gives increased power while controlling the type 1 error, as long as the time trends are equal for the different arms and additive on the model scale. This holds even if the shape of the time trend deviates from a step function when patients are allocated to arms by block randomisation. However, if time trends differ between arms or are not additive to treatment effects in the scale of the model, the type 1 error rate may be inflated. Conclusions The efficiency gained by using step function models to incorporate non-concurrent controls can outweigh potential risks of biases, especially in settings with small sample sizes. Such biases may arise if the model assumptions of equality and additivity of time trends are not satisfied. However, the specifics of the trial, scientific plausibility of different time trends, and robustness of results should be carefully considered.