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Although trials with anti-seizure drugs have not shown anti-epileptogenic or disease-modifying activity in humans, new compounds are on the horizon that may require novel trial designs. We briefly discuss the unique challenges and the available options to identify innovative clinical trial designs that differentiate novel anti-epileptogenic and disease-modifying compounds, preferably early in phase II, from current anti-seizure drugs. The most important challenges of clinical testing of agents for epilepsy prevention include having sufficient preclinical evidence for a suitable agent to proceed with a human trial of an anti-epileptogenic drug, and to demonstrate the feasibility of doing such a trial. Major challenges in trial design to assess agents for disease modification include the choice of suitable study parameters, the identification of a high-risk study population, the type of control, the time and duration of treatment, and a feasible follow-up period.

Abstract Inflammatory bowel diseases (IBD) are highly heterogeneous conditions, varying in clinical manifestations, disease localization, progression, and response to treatment. Failing to account for this heterogeneity can substantially diminish the power of clinical trials and reduce the likelihood of detecting a true effect. In this review, we explore the transformative potential of Bayesian statistics in IBD clinical research, highlighting its ability to provide deeper insights, refine trial design, and facilitate more informed medical decision-making. We explain how Bayesian methods are best incorporated into innovative IBD clinical trial designs, such as single-arm trials utilizing historical data, master protocols, and adaptive trials. In adaptive designs, Bayesian techniques enable dynamic adjustments to sample sizes based on interim data, helping to maintain adequate power while optimizing resource allocation. For network meta-analysis, Bayesian statistics enhance the estimation of treatment effects in complex or sparse data situations by integrating prior knowledge and effectively managing hierarchical models. These methods are also applied in pharmacokinetic decision-making to address inter-patient variability in IBD, offering more accurate predictions of drug concentrations and target attainment at the outset of treatment. A checklist is added for non-specialist readers on how to approach reading an article that employs Bayesian methods, as part of a Users’ Guide to the Literature. Lay Summary This review highlights the potential of Bayesian statistics to improve IBD clinical research by refining trial designs and enabling more meaningful inferences. By using some examples, we explain how prior knowledge, such as historical or placebo data, is incorporated into Bayesian analysis to inform real-time decisions and increase the likelihood of detecting true treatment effects.

Background Randomized rollout trial designs, including stepped wedge designs, are commonly used to examine how well an evidence-based intervention or package is being implemented in community or healthcare settings. The multitude of implementation research questions and specific hypotheses suggest the need for diverse randomized rollout implementation trial designs, assignment principles and procedureds, and statistical modeling. Methods We separate key research questions and identify mixed effect models for randomized implementation rollout trials involving 1) a single implementation strategy that tests how this strategy varies over time and/or resources that are allocated, 2) comparison of two distinct implementation strategies, and 3) three distinct strategies or components tested in a single trial. Appropriate rollout designs, optimal assignment methods, and other design and analysis considerations are discussed for trials of up to three distinct implementation strategies. Results To examine improvement in implementation outcomes we present a Fixed-Length Staggered Rollout Trial Design to examine how well a sustainment period continues to produce outcomes, The Rollout Implementation Optimization (ROIO) methodology illustrates testing for quality improvement. For comparing an existing to new strategy, we focus on a Stepped Wedge design, and for comparing two new strategies we describe a Head-to-Head Rollout trial design. To test for synergy between two components, we introduce a Head-to-Head Rollout trial design, and for testing an existing strategy to a new one followed by a sustainment period, we recommend using a Three-Phase Sequential Rollout Implementation trial design. Modeling choices are described, including options for specifying random effects that capture variations in site and clustering. We discuss comparisons of superiority versus non-inferiority testing and multiple contrasts. To support uses of these six designs and analyses, we provide computational code. Conclusions The large class of randomized rollout implementation trial designs provides rich opportunities to address research questions posed by implementation scientists. Balance in assigning sites to cohorts is important before random assignment to time of transition to a new implementation occurs. Specific hypotheses are tested with mixed effects models where fixed effects include comparisons of implementation conditions and random effects that account for variation in sites and clustering.

A practical impediment in adaptive clinical trials is that outcomes must be observed soon enough to apply decision rules to choose treatments for new patients. For example, if outcomes take up to six weeks to evaluate and the accrual rate is one patient per week, on average three new patients will be accrued while waiting to evaluate the outcomes of the previous three patients. The question is how to treat the new patients. This logistical problem persists throughout the trial. Various ad hoc practical solutions are used, none entirely satisfactory. We focus on this problem in phase I–II clinical trials that use binary toxicity and efficacy, defined in terms of event times, to choose doses adaptively for successive cohorts. We propose a general approach to this problem that treats late-onset outcomes as missing data, uses data augmentation to impute missing outcomes from posterior predictive distributions computed from partial follow-up times and complete outcome data, and applies the design’s decision rules using the completed data. We illustrate the method with two cancer trials conducted using a phase I–II design based on efficacy–toxicity trade-offs, including a computer stimulation study. Supplementary materials for this article are available online.

Randomized controlled trials (RCTs) face challenges such as limited generalizability, insufficient recruitment diversity, and high failure rates, often due to restrictive eligibility criteria and inefficient patient selection. Large language models (LLMs) have shown promise in various clinical tasks, but their potential role in RCT design remains underexplored. This study investigates the ability of LLMs, specifically GPT-4-Turbo-Preview, to assist in designing RCTs that enhance generalizability, recruitment diversity, and reduce failure rates, while maintaining clinical safety and ethical standards. We conducted a noninterventional, observational study analyzing 20 parallel-arm RCTs, comprising 10 completed and 10 registered studies published after January 2024 to mitigate pretraining biases. The LLM was tasked with generating RCT designs based on input criteria, including eligibility, recruitment strategies, interventions, and outcomes. The accuracy of LLM-generated designs was quantitatively assessed by 2 independent clinical experts by comparing them to clinically validated ground truth data from ClinicalTrials.gov. We have conducted statistical analysis using natural language processing-based methods, including Bilingual Evaluation Understudy (BLEU), Recall-Oriented Understudy for Gisting Evaluation (ROUGE)-L, and Metric for Evaluation of Translation with Explicit ORdering (METEOR), for objective scoring on corresponding LLM outputs. Qualitative assessments were performed using Likert scale ratings (1-3) for domains such as safety, clinical accuracy, objectivity or bias, pragmatism, inclusivity, and diversity. The LLM achieved an overall accuracy of 72% in replicating RCT designs. Recruitment and intervention designs demonstrated high agreement with the ground truth, achieving 88% and 93% accuracy, respectively. However, LLMs showed lower accuracy in designing eligibility criteria (55%) and outcomes measurement (53%). Natural language processing statistical analysis reported BLEU=0.04, ROUGE-L=0.20, and METEOR=0.18 on average objective scoring of LLM outputs. Qualitative evaluations showed that LLM-generated designs scored above 2 points and closely matched the original designs in scores across all domains, indicating strong clinical alignment. Specifically, both original and LLM-based designs ranked similarly high in safety, clinical accuracy, and objectivity or bias in published RCTs. Moreover, LLM-based design ranked noninferior to original designs in registered RCTs in multiple domains. In particular, LLMs enhanced diversity and pragmatism, which are key factors in improving RCT generalizability and addressing failure rates. LLMs, such as GPT-4-Turbo-Preview, have demonstrated potential in improving RCT design, particularly in recruitment and intervention planning, while enhancing generalizability and addressing diversity. However, expert oversight and regulatory measures are essential to ensure patient safety and ethical standards. The findings support further integration of LLMs into clinical trial design, although continued refinement is necessary to address limitations in eligibility and outcomes measurement.

Background Awareness of model-based designs for dose-finding studies such as the Continual Reassessment Method (CRM) is now becoming more commonplace amongst clinicians, statisticians and trial management staff. In some settings toxicities can occur a long time after treatment has finished, resulting in extremely long, interrupted, CRM design trials. The Time-to-Event CRM (TiTE-CRM), a modification to the original CRM, accounts for the timing of late-onset toxicities and results in shorter trial duration. In this article, we discuss how to design and deliver a trial using this method, from the grant application stage through to dissemination, using two radiotherapy trials as examples. Methods The TiTE-CRM encapsulates the dose-toxicity relationship with a statistical model. The model incorporates observed toxicities and uses a weight to account for the proportion of completed follow-up of participants without toxicity. This model uses all available data to determine the next participant’s dose and subsequently declare the maximum tolerated dose. We focus on two trials designed by the authors to illustrate practical issues when designing, setting up, and running such studies. Results In setting up a TiTE-CRM trial, model parameters need to be defined and the time element involved might cause complications, therefore looking at operating characteristics through simulations is essential. At the grant application stage, we suggest resources to fund statisticians’ time before funding is awarded and make recommendations for the level of detail to include in funding applications. While running the trial, close contact of all involved staff is required as a dose decision is made each time a participant is recruited. We suggest ways of capturing data in a timely manner and give example code in R for design and delivery of the trial. Finally, we touch upon dissemination issues while the trial is running and upon completion. Conclusion Model-based designs can be complex. We hope this paper will help clinical trial teams to demystify the conduct of TiTE-CRM trials and be a starting point for using this methodology in practice.

Background Machine learning (ML) and artificial intelligence (AI) applications have increased across different stages of clinical research. Their use in clinical trials (CTs) has been discussed but not quantified. Methods A scoping review was conducted by searching PubMed, Embase (Ovid), and Scopus for CTs or protocols. The goal was to understand the extent of ML and AI applications in the design, conduct, and analysis of CTs. Screening was performed on Covidence, with GPT model support. Findings After title/abstract and full-text screening, 108 records were included; in some studies, AI/ML was applied across multiple stages. For the design, 20 studies involved advanced methods, six applied them to stratification, four to treatment selection during randomization, six to participant selection, two for outcome assessment, and two for site selection. Seven studies involved them in the collection and analysis of data from wearable devices, and one for monitoring. More commonly, AI/ML has been used at the analysis stage of 93 CTs; however, limitations in reporting trial objectives make it difficult to distinguish the purpose between primary and exploratory analyses. Interpretation This research identifies a serious mismatch between the potential and actual applications of ML in CTs. Considering the potential benefits of ML in CTs, such underuse could hinder the evolution of CTs toward faster and more efficient approaches.

Osteoporosis has a significant economy, society, and health burden. The recent advancement of available therapies caused ethical questions regarding the use of placebo-controlled studies in osteoporosis. Some specialists believe it is not ethically to subject the study participants to any additional risk when there is already verified effective therapy for the disease and established therapies can significantly decrease the likelihood of osteoporotic fracture.  Accordingly, researchers have expressed ethical concerns over placebo-controlled trials. Here, we have briefly addressed ethical and methodological aspects regarding conducting placebo-control trials as well as potential alternatives.

Biomarker-guided treatment is a rapidly developing area of medicine, where treatment choice is personalised according to one or more of an individual’s biomarker measurements. A number of biomarker-guided trial designs have been proposed in the past decade, including both adaptive and non-adaptive trial designs which test the effectiveness of a biomarker-guided approach to treatment with the aim of improving patient health. A better understanding of them is needed as challenges occur both in terms of trial design and analysis. We have undertaken a comprehensive literature review based on an in-depth search strategy with a view to providing the research community with clarity in definition, methodology and terminology of the various biomarker-guided trial designs (both adaptive and non-adaptive designs) from a total of 211 included papers. In the present paper, we focus on non-adaptive biomarker-guided trial designs for which we have identified five distinct main types mentioned in 100 papers. We have graphically displayed each non-adaptive trial design and provided an in-depth overview of their key characteristics. Substantial variability has been observed in terms of how trial designs are described and particularly in the terminology used by different authors. Our comprehensive review provides guidance for those designing biomarker-guided trials.

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.