Time to STEP UP: methods and findings from the development of guidance to help researchers design inclusive clinical trials.

BACKGROUND: It is important to design clinical trials to include all those who may benefit from the intervention being tested. Several frameworks have been developed to help researchers think about the barriers to inclusion of particular under-served groups when designing a trial, but there is a lack of practical guidance on how to implement these frameworks. This paper describes the ACCESS project, the findings from each phase of the project and the guidance we developed (STEP UP) on how to design more inclusive trials. METHODS: Development of the STEP UP guidance had five phases: (1) Scoping literature review, (2) 'roundtable' discussion meetings, (3) redesign of trials, (4) interviews and (5) guidance document development, with input from public contributors and the ACCESS team. RESULTS: Over 40 experts contributed to the ACCESS project-patients and the public, clinicians, NHS research staff, trialists and other academics. The scoping review identified several strategies being used to improve inclusion, mostly around recruitment settings, but there was little evaluation of these strategies. The 'roundtable' discussions identified additional strategies being used across the UK and Ireland to improve inclusion, which were grouped into: Communication, Community engagement, Recruitment sites, Patient information, Flexibility, Recruitment settings, Consent process, Monitoring, Training for researchers and Incentives. These strategies were used to redesign three existing trials by applying one of the three INCLUDE frameworks (ethnicity, socioeconomic disadvantage, impaired capacity to consent) to one trial each, to produce the key recommendations for the guidance. Issues around implementation were explored in stakeholder interviews and key facilitators were identified: funders requesting information on inclusion, having the time and funding to implement strategies, dedicated staff, flexibility in trial protocols, and considering inclusion of under-served groups at the design stages. The STEP UP guidance is freely available at http://step-up-clinical-trials.co.uk . CONCLUSION: Researchers should consider inclusivity to shape initial trial design decisions. Trial teams and funders need to ensure that trials are given both the resources and time needed to implement the STEP UP guidance and increase the opportunities to recruit a diverse population.

Randomised clinical trials compare one or more treatments to another to see which ones work best. Trials don't always include people or groups who might benefit from the results: those excluded are sometimes called 'under- served groups'. Recent work has shone a light on this and now researchers are being asked by the public, trial funders and others to design their research so that under-served groups are more able to take part.We worked on a project to find out how to make sure everyone can be part of clinical trials. We looked at published work and held five online meetings with researchers, doctors, and patients to see what was being done already, and to think of other things that could help under-served groups take part in trials. Three groups of people, including scientists, patients, doctors and other NHS workers then  used this information to redesign three older trials using some existing inclusivity frameworks to think through the barriers for under-served groups in these trials. The three groups then talked through these trials at a 2-hour meeting, suggesting changes to the original trial plan, and discussed whether the suggestions were practical and useful. From this we came up with recommendations for how to design trials so that they have fewer barriers for under-served groups.We interviewed people to find out the best way to put these things into practice and talk through any practical issues. Using all of this information: the recommendations and what came out of the interviews, the study team created some guidance ­ 'STEP UP (Strategies for Trialists to promote Equal Participation in clinical trials for Under-served Populations)' ­ for people working in trials.

Three-outcome designs for external pilot trials with progression criteria.

BACKGROUND: Whether or not to progress from a pilot study to a definitive trial is often guided by pre-specified quantitative progression criteria with three possible outcomes. Although the choice of these progression criteria will help to determine the statistical properties of the pilot trial, there is a lack of research examining how they, or the pilot sample size, should be determined. METHODS: We review three-outcome trial designs originally proposed in the phase II oncology setting and extend these to the case of external pilots, proposing a unified framework based on univariate hypothesis tests and the control of frequentist error rates. We apply this framework to an example and compare against a simple two-outcome alternative. RESULTS: We find that three-outcome designs can be used in the pilot setting, although they are not generally more efficient than simpler two-outcome alternatives. We show that three-outcome designs can help allow for other sources of information or other stakeholders to feed into progression decisions in the event of a borderline result, but this will come at the cost of a larger pilot sample size than the two-outcome case. We also show that three-outcome designs can be used to allow adjustments to be made to the intervention or trial design before commencing the definitive trial, providing the effect of the adjustment can be accurately predicted at the pilot design stage. An R package, tout, is provided to optimise progression criteria and pilot sample size. CONCLUSIONS: The proposed three-outcome framework provides a way to optimise pilot trial progression criteria and sample size in a way that leads to desired operating characteristics. It can be applied whether or not an adjustment following the pilot trial is anticipated, but will generally lead to larger sample size requirements than simpler two-outcome alternatives.

Improving analysis of cognitive outcomes in cardiovascular trials using different statistical approaches.

BACKGROUND: The Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) questionnaires are commonly used to measure global cognition in clinical trials. Because these scales are discrete and bounded with ceiling and floor effects and highly skewed, their analysis as continuous outcomes presents challenges. Normality assumptions of linear regression models are usually violated, which may result in failure to detect associations with variables of interest. METHODS: Alternative approaches to analyzing the results of these cognitive batteries include transformations (standardization, square root, or log transformation) of the scores in the multivariate linear regression (MLR) model, the use of nonlinear beta-binomial regression (which is not dependent on the assumption of normality), or Tobit regression, which adds a latent variable to account for bounded data. We aim to empirically compare the model performance of all proposed approaches using four large randomized controlled trials (ORIGIN, TRANSCEND, COMPASS, and NAVIGATE-ESUS), and using as metrics the Akaike information criterion (AIC). We also compared the treatment effects for the methods that have the same unit of measure (i.e., untransformed MLR, beta-binomial, and Tobit). RESULTS: The beta-binomial consistently demonstrated superior model performance, with the lowest AIC values among nearly all the approaches considered, followed by the MLR with square root and log transformations across all four studies. Notably, in ORIGIN, a substantial AIC reduction was observed when comparing the untransformed MLR to the beta-binomial, whereas other studies had relatively small AIC reductions. The beta-binomial model also resulted in a significant treatment effect in ORIGIN, while the untransformed MLR and Tobit regression showed no significance. The other three studies had similar and insignificant treatment effects among the three approaches. CONCLUSION: When analyzing discrete and bounded outcomes, such as cognitive scores, as continuous variables, a beta-binomial regression model improves model performance, avoids spurious significance, and allows for a direct interpretation of the actual cognitive measure. TRIALS REGISTRATION: ORIGIN (NCT00069784). Registered on October 1, 2003; TRANSCEND (NCT00153101). Registered on September 9, 2005; COMPASS (NCT01776424). Registered on January 24, 2013; NAVIGATE-ESUS (NCT02313909). Registered on December 8, 2014.

Exploring the design of clinical research studies on the efficacy mechanisms in type 2 diabetes mellitus.

This review examines the complexities of Type 2 Diabetes Mellitus (T2DM), focusing on the critical role of integrating omics technologies with traditional experimental methods. It underscores the advancements in understanding the genetic diversity of T2DM and emphasizes the evolution towards personalized treatment modalities. The paper analyzes a variety of omics approaches, including genomics, methylation, transcriptomics, proteomics, metabolomics, and intestinal microbiomics, delineating their substantial contributions to deciphering the multifaceted mechanisms underlying T2DM. Furthermore, the review highlights the indispensable role of non-omics experimental techniques in comprehending and managing T2DM, advocating for their integration in the development of tailored medicine and precision treatment strategies. By identifying existing research gaps and suggesting future research trajectories, the review underscores the necessity for a comprehensive, multidisciplinary approach. This approach synergistically combines clinical insights with cutting-edge biotechnologies, aiming to refine the management and therapeutic interventions of T2DM, and ultimately enhancing patient outcomes. This synthesis of knowledge and methodologies paves the way for innovative advancements in T2DM research, fostering a deeper understanding and more effective treatment of this complex condition.

Virtual simulation for teaching cardiology in nursing: a scoping review protocol.

INTRODUCTION: Virtual simulation (VS) can be an effective learning strategy in the context of nursing education on cardiovascular disease; however, its use in teaching cardiology in nursing is less studied. The objective of this scoping review is to map the use of VS for teaching cardiology in nursing. METHODS AND ANALYSIS: This scoping review will be conducted according to the Joanna Briggs Institute methods, and the results will be reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist. Eight databases will be searched: MEDLINE (NCBI/PubMed), Cumulative Index to Nursing and Allied Health Literature, Web of Science, Latin American and Caribbean Literature in Health Sciences, Spanish Bibliographic Index of Health Sciences, Database of Nursing, EMBASE and Google Scholar from inception to 31 July 2024. This study will include any existing peer-reviewed literature and grey literature. There will be no time or language restrictions. Two reviewers will screen and select the articles independently, and when there are differences, they will be resolved with a third opinion. When appropriate, broad themes and categories derived from the review questions will be accompanied by other illustrative formats (eg, tables or graphs, word clouds and infographics). ETHICS AND DISSEMINATION: This research project does not require ethical committee approval. The study is part of a cooperative research project between researchers from the Federal University of Piauí, Northeast of Brazil, and Queen's University, Ontario, Canada, to develop and seek evidence of content validity of a VS game about valvular heart disease. The protocol and review will be published in peer-reviewed journals. REGISTRATION DETAILS: Open Science Framework (https://doi.org/10.17605/OSF.IO/S3UMH).

Utility of respiratory viral testing in the risk stratification of young febrile infants presenting to emergency care settings: a protocol for systematic review and meta-analysis.

INTRODUCTION: Febrile infants under 3 months of age are at risk of invasive bacterial infection (IBI). It is currently unclear if testing for respiratory viruses may have a role in IBI risk stratification. If found to be associated with the likelihood of IBI, respiratory viral point-of-care testing may improve patient and caregiver experience, reduce costs and enhance antimicrobial stewardship. METHODS AND ANALYSIS: This is a study protocol for a systematic review and meta-analysis that aims to answer the following question: In young febrile infants presenting to emergency care settings does a positive respiratory viral test for RSV, Influenza or SARS-CoV2 (relative to a negative test) add value to current risk stratification pathways for the exclusion of invasive bacterial infection, subsequently enabling safe de-escalation of investigation and treatment?A search strategy will include MEDLINE, EMBASE, Web of Science, The Cochrane Library and grey literature. Abstracts and then full texts will be independently screened for selection. Data extraction and quality assessment will be completed by two independent authors.The primary objective is to analyse the ability of a positive respiratory viral test to identify the overall risk of IBI. The secondary objective is to perform a subgroup analysis to investigate how the risk stratification alters based on other variables including virus type, patient characteristics and the presence of an identified source of fever.Bivariate random-effects meta-analysis will be undertaken. Diagnostic odds ratios (OR), sensitivity, specificity and positive and negative likelihood ratios will be calculated. The degree of heterogeneity and publication bias will be investigated and presented. ETHICS AND DISSEMINATION: Ethical approval is not required. We will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to disseminate the study results through publication and conference presentations. PROSPERO REGISTRATION NUMBER: This protocol is registered in PROSPERO-ID number: CRD42023433716.

A clinical protocol for a German birth cohort study of the Maturation of Immunity Against respiratory viral Infections (MIAI).

Introduction: Respiratory viral infections (RVIs) are a major global contributor to morbidity and mortality. The susceptibility and outcome of RVIs are strongly age-dependent and show considerable inter-population differences, pointing to genetically and/or environmentally driven developmental variability. The factors determining the age-dependency and shaping the age-related changes of human anti-RVI immunity after birth are still elusive. Methods: We are conducting a prospective birth cohort study aiming at identifying endogenous and environmental factors associated with the susceptibility to RVIs and their impact on cellular and humoral immune responses against the influenza A virus (IAV), respiratory syncytial virus (RSV) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The MIAI birth cohort enrolls healthy, full-term neonates born at the University Hospital Würzburg, Germany, with follow-up at four defined time-points during the first year of life. At each study visit, clinical metadata including diet, lifestyle, sociodemographic information, and physical examinations, are collected along with extensive biomaterial sampling. Biomaterials are used to generate comprehensive, integrated multi-omics datasets including transcriptomic, epigenomic, proteomic, metabolomic and microbiomic methods. Discussion: The results are expected to capture a holistic picture of the variability of immune trajectories with a focus on cellular and humoral key players involved in the defense of RVIs and the impact of host and environmental factors thereon. Thereby, MIAI aims at providing insights that allow unraveling molecular mechanisms that can be targeted to promote the development of competent anti-RVI immunity in early life and prevent severe RVIs. Clinical trial registration: https://drks.de/search/de/trial/, identifier DRKS00034278.

Multiple imputation using auxiliary imputation variables that only predict missingness can increase bias due to data missing not at random.

BACKGROUND: Epidemiological and clinical studies often have missing data, frequently analysed using multiple imputation (MI). In general, MI estimates will be biased if data are missing not at random (MNAR). Bias due to data MNAR can be reduced by including other variables ("auxiliary variables") in imputation models, in addition to those required for the substantive analysis. Common advice is to take an inclusive approach to auxiliary variable selection (i.e. include all variables thought to be predictive of missingness and/or the missing values). There are no clear guidelines about the impact of this strategy when data may be MNAR. METHODS: We explore the impact of including an auxiliary variable predictive of missingness but, in truth, unrelated to the partially observed variable, when data are MNAR. We quantify, algebraically and by simulation, the magnitude of the additional bias of the MI estimator for the exposure coefficient (fitting either a linear or logistic regression model), when the (continuous or binary) partially observed variable is either the analysis outcome or the exposure. Here, "additional bias" refers to the difference in magnitude of the MI estimator when the imputation model includes (i) the auxiliary variable and the other analysis model variables; (ii) just the other analysis model variables, noting that both will be biased due to data MNAR. We illustrate the extent of this additional bias by re-analysing data from a birth cohort study. RESULTS: The additional bias can be relatively large when the outcome is partially observed and missingness is caused by the outcome itself, and even larger if missingness is caused by both the outcome and the exposure (when either the outcome or exposure is partially observed). CONCLUSIONS: When using MI, the naïve and commonly used strategy of including all available auxiliary variables should be avoided. We recommend including the variables most predictive of the partially observed variable as auxiliary variables, where these can be identified through consideration of the plausible casual diagrams and missingness mechanisms, as well as data exploration (noting that associations with the partially observed variable in the complete records may be distorted due to selection bias).

Standardised reporting of burden of disease studies: the STROBOD statement.

BACKGROUND: The burden of disease (BOD) approach, originating with the Global Burden of Disease (GBD) study in the 1990s, has become a cornerstone for population health monitoring. Despite the widespread use of the Disability-Adjusted Life Year (DALY) metric, variations in methodological approaches and reporting inconsistencies hinder comparability across studies. To tackle this issue, we set out to develop guidelines for reporting DALY calculation studies to improve the transparency and comparability of BOD estimates. METHODS AND FINDINGS: The development of the STROBOD statement began within the European Burden of Disease Network, evolving from initial concepts discussed in workshops and training sessions focused on critical analysis of BOD studies. In 2021, a working group was formed to refine the preliminary version into the final Standardised Reporting of Burden of Disease studies (STROBOD) statement, consisting of 28 items structured across six main sections. These sections cover the title, abstract, introduction, methods, results, discussion, and open science, aiming to ensure transparency and standardization in reporting BOD studies. Notably, the methods section of the STROBOD checklist encompasses aspects such as study setting, data inputs and adjustments, DALY calculation methods, uncertainty analyses, and recommendations for reproducibility and transparency. A pilot phase was conducted to test the efficacy of the STROBOD statement, highlighting the importance of providing clear explanations and examples for each reporting item. CONCLUSIONS: The inaugural STROBOD statement offers a crucial framework for standardizing reporting in BOD research, with plans for ongoing evaluation and potential revisions based on user feedback. While the current version focuses on general BOD methodology, future iterations may include specialized checklists for distinct applications such as injury or risk factor estimation, reflecting the dynamic nature of this field.

Design and rationale for an empirical investigation of the resource use and costs of investigator-initiated randomized trials in Switzerland, the UK, and Germany.

BACKGROUND: Conducting high-quality randomized clinical trials (RCTs) is challenging, time consuming, and resource intense. Academic investigators usually depend on scarce financial resources; however, current literature lacks systematically collected empirical data on the detailed resource use and costs of investigator-initiated RCTs. METHODS: The aim of this study is to generate a database of detailed empirical resource use and cost data from 100 investigator-initiated RCTs in Switzerland, Germany, and the UK. Investigators enter their empirical costs data into an online data collection form, which is followed by a short interview and a detailed cost report. We plan to investigate cost patterns and cost drivers and examine planned versus actual RCT costs as well as explore different strata of costs across the planning, conduct, and finalization phases, in drug and non-drug trials, and across medical fields and countries. DISCUSSION: This study will add detailed empirical data to the limited research on investigator-initiated RCT costs currently available. A study limitation will be that cost data will be retrospective and self-reported, which might be inaccurate depending on how costs were recorded. TRIAL REGISTRATION: Open Science Framework (OSF) https://doi.org/10.17605/OSF.IO/QY2GU . Registered on June 4, 2021.

Consideration of factors of low accrual and methods for setting appropriate accrual periods: Japan Clinical Oncology Group study.

BACKGROUND: Poor patient accrual can delay reporting of clinical trials and, consequently, the development of new treatments. For reducing the risk of additional resource requirements, a method for setting planned accrual periods with minimal deviation from the actual accrual periods is desirable. Risk factors for poor patient accrual and the appropriate method of estimating the required accrual period for timely completion of clinical trials were evaluated using the data of trials conducted by the Japan Clinical Oncology Group. METHODS: The study included 199 trials that started patient accrual between January 1, 1990, and June 30, 2021. The explanatory variables included factors that could be evaluated prior to trial commencement. We also evaluated whether the estimation methods for accrual pace could lead to completion within the planned accrual period. RESULTS: Approximately 23.6% of trials were completed within the planned accrual period. The risk factors for trial extension included planned accrual periods > 3 years (reference group: ≤ 3 years, odds ratio [OR] 0.37, 95% confidence interval [CI]: 0.15-0.92, P = 0.033) and stratified trial design (reference group: nonrandomized phase II trials, nonrandomized phase III trial [OR: 3.28, 95% CI: 0.99-10.9, P = 0.051], randomized phase II trial [OR: 3.91, 95% CI: 0.75-20.30, P = 0.105], and randomized phase III trial [OR: 9.29, 95% CI: 3.39-25.40, P < 0.001]). The method of estimating the accrual pace based on past clinical trials facilitated timely completion of the trial (OR: 3.51; 95% CI: 1.73-7.10, P < 0.001), unlike the estimation method based on survey evaluation of the accrual pace for participating institutions (OR: 1.12, 95% CI: 0.56-2.26, P = 0.751). Furthermore, the discrepancy between planned and actual accrual periods was minimal when using the methods of considering the accrual pace of past clinical trials. CONCLUSIONS: Considering the accrual pace of past clinical trials is useful for estimating the required accrual period if data from past trials are available. When conducting a survey, it is necessary to be cautious of overestimating the cases at each facility.

The Prevalent New-User Design to Study Drug-Drug Interactions: The Example of Sulfonylureas and Warfarin Interaction on the Risk of Severe Hypoglycemia.

PURPOSE: The optimal design for pharmacoepidemiologic drug-drug interactions (DDIs) studies is unclear. Using the association between concomitant use of sulfonylureas and warfarin and the risk of severe hypoglycemia as a case study, a DDI with little or no clinical impact, we tested whether the prevalent new-user design can be applied in the area. METHODS: Among all patients initiating sulfonylureas in the UK's Clinical Practice Research Datalink (1998-2020), we identified those adding-on warfarin while on a sulfonylurea. For each co-exposed patient, we defined a prescription-based exposure set including other sulfonylurea users not adding-on warfarin (comparators). Within each exposure set, we matched each co-exposed patient to five comparators on time-conditional propensity scores (TCPS) and followed them using an as-treated approach. Cox proportional hazards models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) of severe hypoglycemia associated with concomitant use of sulfonylureas and warfarin compared to use of sulfonylureas alone. Sensitivity analyses addressed the impact of different potential sources of bias. RESULTS: The study cohort included 17 890 patients co-exposed to sulfonylureas and warfarin and 88 749 matched comparators. After TCPS matching, patient characteristics were well-balanced between groups. Compared to use of sulfonylureas alone, concomitant use of sulfonylureas and warfarin was not associated with the risk of severe hypoglycemia (HR, 1.04; 95% CI, 0.92-1.17). Sensitivity analyses were consistent with the primary analysis (HRs ranging from 1.01 to 1.15, all not statistically significant). CONCLUSIONS: Our study suggests that the prevalent new-user design could be used for the assessment of clinical effects of DDIs.

Core Concepts in Pharmacoepidemiology: Quantitative Bias Analysis.

Pharmacoepidemiological studies provide important information on the safety and effectiveness of medications, but the validity of study findings can be threatened by residual bias. Ideally, biases would be minimized through appropriate study design and statistical analysis methods. However, residual biases can remain, for example, due to unmeasured confounders, measurement error, or selection into the study. A group of sensitivity analysis methods, termed quantitative bias analyses, are available to assess, quantitatively and transparently, the robustness of study results to these residual biases. These approaches include methods to quantify how the estimated effect would be altered under specified assumptions about the potential bias, and methods to calculate bounds on effect estimates. This article introduces quantitative bias analyses for unmeasured confounding, misclassification, and selection bias, with a focus on their relevance and application to pharmacoepidemiological studies.

Challenges and opportunities for early phase clinical trials of novel drug-radiotherapy combinations: recommendations from NRG Oncology, the American Society for Radiation Oncology (ASTRO), the American College of Radiology (ACR), the Sarah Cannon Research Institute, and the American College of Radiation Oncology (ACRO).

NRG Oncology's Developmental Therapeutics and Radiation Therapy Subcommittee assembled an interdisciplinary group of investigators to address barriers to successful early phase clinical trials of novel combination therapies involving radiation. This Policy Review elucidates some of the many challenges associated with study design for early phase trials combining radiotherapy with novel systemic agents, which are distinct from drug-drug combination development and are often overlooked. We also advocate for potential solutions that could mitigate or eliminate some of these barriers, providing examples of specific clinical trial designs that could help facilitate efficient and effective evaluation of novel drug-radiotherapy combinations.

Study design and procedures in the incontinence post robot-assisted radical prostatectomy: anatomical and functional causes (IPA) - a prospective observational clinical trial.

OBJECTIVE: To describe the study design and procedures of the incontinence post robot- assisted radical prostatectomy, anatomical and functional causes (IPA) trial. This trial aims to identify and study patient and procedure specific factors leading to urinary incontinence post robot-assisted laparoscopic radical prostatectomy (RALP). MATERIAL AND METHODS: The IPA study is a prospective, multicentre, open non-randomised surgical trial, including patients prior to RALP and registered on-line (ISRCTN67297115). IPA is administered from the Department of Urology at Sahlgrenska University Hospital, Gothenburg, Sweden. Patients undergo an anatomical and functional evaluation using magnetic resonance imaging (MRI), urodynamics including cystometry, pressure-flow and urethral pressure profile, and dynamic transrectal ultrasound prior to and 3 months after RALP. The incontinence data are gathered using patient reported outcome measure questionnaires. The primary endpoint is incontinence at 3 months after RALP, defined as need of any pad. The secondary endpoints are incontinence 12 months post RALP defined as need of any pad, and 3- and 12-months post RALP, defined as use of more than a safety pad. RESULTS: Until October 2023, 207 patients have been included of the stipulated 1,000, with an increasing rate of accrual. Out of these patients,187 have had a pre- and post-operative MRI and 177 have undergone pre- and post-operative urodynamics. CONCLUSIONS: The design of the IPA study, together with promising accrual and coming multicentre inclusion, will hopefully result in the identification, and deeper understanding, of the various risk-factors for post-RALP incontinence. This could improve information and decision making regarding adequate treatment for patients with prostate cancer.

The tyranny of non-inferiority trials.

Opportunities to decrease the toxicity and cost of approved treatment regimens with lower dose, less frequent, or shorter duration alternative regimens have been limited by the perception that alternatives must be non-inferior to approved regimens. Non-inferiority trials are large and expensive to do, because they must show statistically that the alternative and approved therapies differ in a single outcome, by a margin far smaller than that required to demonstrate superiority. Non-inferiority's flaws are manifest: it ignores variability expected to occur with repeated evaluation of the approved therapy, fails to recognise that a trial of similar design will be labelled as superiority or non-inferiority depending on whether it is done prior to or after initial registration of the approved treatment, and relegates endpoints such as toxicity and cost. For example, while a less toxic and less costly regimen of 3 months duration would typically be required to demonstrate efficacy that is non-inferior to that of a standard regimen of 6 months to displace it, the longer duration therapy has no such obligation to prove its superiority. This situation is the tyranny of the non-inferiority trial: its statistics perpetuate less cost-effective regimens, which are not patient-centred, even when less intensive therapies confer survival benefits nearly identical to those of the standard, by placing a disproportionately large burden of proof on the alternative. This approach is illogical. We propose that the designation of trials as superiority or non-inferiority be abandoned, and that randomised, controlled trials should henceforth be described simply as "comparative".

Study design: think 'scientific value' not 'p-values'.

Statistically based experimental designs have been available for over a century. However, many preclinical researchers are completely unaware of these methods, and the success of experiments is usually equated only with 'p < 0.05'. By contrast, a well-thought-out experimental design strategy provides data with evidentiary and scientific value. A value-based strategy requires implementation of statistical design principles coupled with basic project management techniques. This article outlines the three phases of a value-based design strategy: proper framing of the research question, statistically based operationalisation through careful selection and structuring of appropriate inputs, and incorporation of methods that minimise bias and process variation. Appropriate study design increases study validity and the evidentiary strength of the results, reduces animal numbers, and reduces waste from noninformative experiments. Statistically based experimental design is thus a key component of the 'Reduction' pillar of the 3R (Replacement, Reduction, Refinement) principles for ethical animal research.

Methods for applying blinding and randomisation in animal experiments.

Blinding and randomisation are important methods for increasing the robustness of pre-clinical studies, as incomplete or improper implementation thereof is recognised as a source of bias. Randomisation ensures that any known and unknown covariates introducing bias are randomly distributed over the experimental groups. Thereby, differences between the experimental groups that might otherwise have contributed to false positive or -negative results are diminished. Methods for randomisation range from simple randomisation (e.g. rolling a dice) to advanced randomisation strategies involving the use of specialised software. Blinding on the other hand ensures that researchers are unaware of group allocation during the preparation, execution and acquisition and/or the analysis of the data. This minimises the risk of unintentional influences resulting in bias. Methods for blinding require strong protocols and a team approach. In this review, we outline methods for randomisation and blinding and give practical tips on how to implement them, with a focus on animal studies.

Prospective cohort for early detection of liver cancer (Pearl): a study protocol.

INTRODUCTION: Hepatocellular carcinoma (HCC) is the fastest-rising and fourth most common cause of cancer death worldwide. Liver cirrhosis is the largest underlying risk factor for HCC. Therefore, patients with cirrhosis should have regular ultrasound and biochemical screening to pick up early HCC. Early HCC can be cured; more advanced HCCs have limited treatment options and poor prognosis. Current screening methods are suboptimal with poor sensitivity in picking up early disease. In this study, the investigators aim to recruit people with liver cirrhosis into a Prospective cohort for early detection of liver cancer-the Pearl cohort. The investigators believe that by using state-of-the-art tests we can improve the detection of early HCC. METHODS AND ANALYSIS: This is a UK-based prospective, longitudinal, diagnostic, prognostic, multicentre, non-CTIMP study. Aiming to recruit 3000 patients with liver cirrhosis without a HCC diagnosis, the Pearl cohort will be followed actively for 3 years from recruitment and then passively via registry data for ten years thereafter. Blood and urine samples will be taken and information from routine care will be gathered. These will be used to assess novel diagnostic approaches for the detection early HCC and to develop models to identify those most at risk for developing HCC.Participants will be linked to national UK health registries to ensure long-term capture of HCC incidence and other relevant endpoints. Approximately 75 patients are predicted to develop de novo HCC within the 3-year follow up period. After this period, the study teams will obtain data on participants for at least 10 years after the last contact. This cohort will help develop an understanding of the incidence of HCC in a UK population stratified by underlying cirrhosis aetiology. ETHICS AND DISSEMINATION: Ethical approval has been granted by REC and the trial is registered on ClinicalTrials.gov. The results will be published in peer-reviewed journals and presented at relevant meetings. TRIAL REGISTRATION NUMBER: NCT05541601.

Prospectively investigating the impact of AI onshared decision-making in post kidney transplant care (PRIMA-AI): protocol for a longitudinal qualitative study among patients, their support persons and treating physicians at a tertiary care centre.

INTRODUCTION: As healthcare is shifting from a paternalistic to a patient-centred approach, medical decision making becomes more collaborative involving patients, their support persons (SPs) and physicians. Implementing shared decision-making (SDM) into clinical practice can be challenging and becomes even more complex with the introduction of artificial intelligence (AI) as a potential actant in the communicative network. Although there is more empirical research on patients' and physicians' perceptions of AI, little is known about the impact of AI on SDM. This study will help to fill this gap. To the best of our knowledge, this is the first systematic empirical investigation to prospectively assess the views of patients, their SPs and physicians on how AI affects SDM in physician-patient communication after kidney transplantation. Using a transdisciplinary approach, this study will explore the role and impact of an AI-decision support system (DSS) designed to assist with medical decision making in the clinical encounter. METHODS AND ANALYSIS: This is a plan to roll out a 2 year, longitudinal qualitative interview study in a German kidney transplant centre. Semi-structured interviews with patients, SPs and physicians will be conducted at baseline and in 3-, 6-, 12- and 24-month follow-up. A total of 50 patient-SP dyads and their treating physicians will be recruited at baseline. Assuming a dropout rate of 20% per year, it is anticipated that 30 patient-SP dyads will be included in the last follow-up with the aim of achieving data saturation. Interviews will be audio-recorded and transcribed verbatim. Transcripts will be analysed using framework analysis. Participants will be asked to report on their (a) communication experiences and preferences, (b) views on the influence of the AI-based DSS on the normative foundations of the use of AI in medical decision-making, focusing on agency along with trustworthiness, transparency and responsibility and (c) perceptions of the use of the AI-based DSS, as well as barriers and facilitators to its implementation into routine care. ETHICS AND DISSEMINATION: Approval has been granted by the local ethics committee of Charité-Universitätsmedizin Berlin (EA1/177/23 on 08 August 2023). This research will be conducted in accordance with the principles of the Declaration of Helsinki (1996). The study findings will be used to develop communication guidance for physicians on how to introduce and sustainably implement AI-assisted SDM. The study results will also be used to develop lay language patient information on AI-assisted SDM. A broad dissemination strategy will help communicate the results of this research to a variety of target groups, including scientific and non-scientific audiences, to allow for a more informed discourse among different actors from policy, science and society on the role and impact of AI in physician-patient communication.