c4c: Paediatric pharmacovigilance: Methodological considerations in research and development of medicines for children - A c4c expert group white paper.

Children frequently respond differently to therapies compared to adults. Differences also exist between paediatric age groups for pharmacokinetics and pharmacodynamics in both efficacy and safety. Paediatric pharmacovigilance requires an understanding of the unique aspects of children with regard to, for example, drug response, growth and development, clinical presentation of adverse drug reactions (ADRs), how they can be detected and population-specific factors (e.g., more frequent use of off-label/unlicensed drugs). In recognition of these challenges, a group of experts has been formed in the context of the conect4children (c4c) project to support paediatric drug development. This expert group collaborated to develop methodological considerations for paediatric drug safety and pharmacovigilance throughout the life-cycle of medicinal products which are described in this article. These considerations include practical points to consider for the development of the paediatric section of the risk management plan (RMP), safety in paediatric protocol development, safety data collection and analysis. Furthermore, they describe the specific details of post-marketing pharmacovigilance in children using, for example, spontaneous reports, electronic health care records, registries and record-linkage, as well as the use of paediatric pharmacoepidemiology studies for risk characterisation. Next the details of the assessment of benefit-risk and challenges related to medicinal product formulation in the context of a Paediatric Investigation Plan (PIP) are presented. Finally, practical issues in paediatric signal detection and evaluation are included. This paper provides practical points to consider for paediatric pharmacovigilance throughout the life-cycle of medicinal products for RMPs, protocol development, safety data collection and analysis and PIPs.

Efficacy and safety of arimoclomol in Niemann-Pick disease type C: Results from a double-blind, randomised, placebo-controlled, multinational phase 2/3 trial of a novel treatment.

Niemann-Pick disease type C (NPC) is a rare, genetic, progressive neurodegenerative disorder with high unmet medical need. We investigated the safety and efficacy of arimoclomol, which amplifies the heat shock response to target NPC protein misfolding and improve lysosomal function, in patients with NPC. In a 12-month, prospective, randomised, double-blind, placebo-controlled, phase 2/3 trial (ClinicalTrials.gov identifier: NCT02612129), patients (2-18 years) were randomised 2:1 to arimoclomol:placebo, stratified by miglustat use. Routine clinical care was maintained. Arimoclomol was administered orally three times daily. The primary endpoint was change in 5-domain NPC Clinical Severity Scale (NPCCSS) score from baseline to 12 months. Fifty patients enrolled; 42 completed. At month 12, the mean progression from baseline in the 5-domain NPCCSS was 0.76 with arimoclomol vs 2.15 with placebo. A statistically significant treatment difference in favour of arimoclomol of -1.40 (95% confidence interval: -2.76, -0.03; P = .046) was observed, corresponding to a 65% reduction in annual disease progression. In the prespecified subgroup of patients receiving miglustat as routine care, arimoclomol resulted in stabilisation of disease severity over 12 months with a treatment difference of -2.06 in favour of arimoclomol (P = .006). Adverse events occurred in 30/34 patients (88.2%) receiving arimoclomol and 12/16 (75.0%) receiving placebo. Fewer patients had serious adverse events with arimoclomol (5/34, 14.7%) vs placebo (5/16, 31.3%). Treatment-related serious adverse events (n = 2) included urticaria and angioedema. Arimoclomol provided a significant and clinically meaningful treatment effect in NPC and was well tolerated.

Value of information methods to design a clinical trial in a small population to optimise a health economic utility function.

BACKGROUND: Most confirmatory randomised controlled clinical trials (RCTs) are designed with specified power, usually 80% or 90%, for a hypothesis test conducted at a given significance level, usually 2.5% for a one-sided test. Approval of the experimental treatment by regulatory agencies is then based on the result of such a significance test with other information to balance the risk of adverse events against the benefit of the treatment to future patients. In the setting of a rare disease, recruiting sufficient patients to achieve conventional error rates for clinically reasonable effect sizes may be infeasible, suggesting that the decision-making process should reflect the size of the target population. METHODS: We considered the use of a decision-theoretic value of information (VOI) method to obtain the optimal sample size and significance level for confirmatory RCTs in a range of settings. We assume the decision maker represents society. For simplicity we assume the primary endpoint to be normally distributed with unknown mean following some normal prior distribution representing information on the anticipated effectiveness of the therapy available before the trial. The method is illustrated by an application in an RCT in haemophilia A. We explicitly specify the utility in terms of improvement in primary outcome and compare this with the costs of treating patients, both financial and in terms of potential harm, during the trial and in the future. RESULTS: The optimal sample size for the clinical trial decreases as the size of the population decreases. For non-zero cost of treating future patients, either monetary or in terms of potential harmful effects, stronger evidence is required for approval as the population size increases, though this is not the case if the costs of treating future patients are ignored. CONCLUSIONS: Decision-theoretic VOI methods offer a flexible approach with both type I error rate and power (or equivalently trial sample size) depending on the size of the future population for whom the treatment under investigation is intended. This might be particularly suitable for small populations when there is considerable information about the patient population.

Missing data handling in non-inferiority and equivalence trials: A systematic review.

BACKGROUND: Non-inferiority (NI) and equivalence clinical trials test whether a new treatment is therapeutically no worse than, or equivalent to, an existing standard of care. Missing data in clinical trials have been shown to reduce statistical power and potentially bias estimates of effect size; however, in NI and equivalence trials, they present additional issues. For instance, they may decrease sensitivity to differences between treatment groups and bias toward the alternative hypothesis of NI (or equivalence). AIMS: Our primary aim was to review the extent of and methods for handling missing data (model-based methods, single imputation, multiple imputation, complete case), the analysis sets used (Intention-To-Treat, Per-Protocol, or both), and whether sensitivity analyses were used to explore departures from assumptions about the missing data. METHODS: We conducted a systematic review of NI and equivalence trials published between May 2015 and April 2016 by searching the PubMed database. Articles were reviewed primarily by 2 reviewers, with 6 articles reviewed by both reviewers to establish consensus. RESULTS: Of 109 selected articles, 93% reported some missing data in the primary outcome. Among those, 50% reported complete case analysis, and 28% reported single imputation approaches for handling missing data. Only 32% reported conducting analyses of both intention-to-treat and per-protocol populations. Only 11% conducted any sensitivity analyses to test assumptions with respect to missing data. CONCLUSION: Missing data are common in NI and equivalence trials, and they are often handled by methods which may bias estimates and lead to incorrect conclusions.

Approaches to sample size calculation for clinical trials in rare diseases.

We discuss 3 alternative approaches to sample size calculation: traditional sample size calculation based on power to show a statistically significant effect, sample size calculation based on assurance, and sample size based on a decision-theoretic approach. These approaches are compared head-to-head for clinical trial situations in rare diseases. Specifically, we consider 3 case studies of rare diseases (Lyell disease, adult-onset Still disease, and cystic fibrosis) with the aim to plan the sample size for an upcoming clinical trial. We outline in detail the reasonable choice of parameters for these approaches for each of the 3 case studies and calculate sample sizes. We stress that the influence of the input parameters needs to be investigated in all approaches and recommend investigating different sample size approaches before deciding finally on the trial size. Highly influencing for the sample size are choice of treatment effect parameter in all approaches and the parameter for the additional cost of the new treatment in the decision-theoretic approach. These should therefore be discussed extensively.

Common Elements in Rare Kidney Diseases: Conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference.

Rare kidney diseases encompass at least 150 different conditions, most of which are inherited. Although individual rare kidney diseases raise specific issues, as a group these rare diseases can have overlapping challenges in diagnosis and treatment. These challenges include small numbers of affected patients, unidentified causes of disease, lack of biomarkers for monitoring disease progression, and need for complex care. To address common clinical and patient issues among rare kidney diseases, the KDIGO Controversies Conference entitled, Common Elements in Rare Kidney Diseases, brought together a panel of multidisciplinary clinical providers and patient advocates to address five central issues for rare kidney diseases. These issues encompassed diagnostic challenges, management of kidney functional decline and progression of chronic kidney disease, challenges in clinical study design, translation of advances in research to clinical care, and provision of practical and integrated patient support. Thus, by a process of consensus, guidance for addressing these challenges was developed and is presented here.

Statistical reporting of clinical pharmacology research.

Research in clinical pharmacology covers a wide range of experiments, trials and investigations: clinical trials, systematic reviews and meta-analyses of drug usage after market approval, the investigation of pharmacokinetic-pharmacodynamic relationships, the search for mechanisms of action or for potential signals for efficacy and safety using biomarkers. Often these investigations are exploratory in nature, which has implications for the way the data should be analysed and presented. Here we summarize some of the statistical issues that are of particular importance in clinical pharmacology research.

Evidence-Based Medicine and Rare Diseases.

This chapter discusses the meaning of evidence-based medicine and where it relates to randomised controlled trials, but also where it does not. The need for good quality evidence is stressed through a discussion of high failure rates in drug development and arguments against access to unlicensed (and largely untested) treatments are set out (despite the good intentions of those who advocate such access to treatments).Good quality, reliable evidence does not always have to come from clinical trials. Other forms of evidence are discussed. Meta-analyses of individual trials may help to resolve the problem that, in rare diseases, it may be very difficult or impossible to do adequately powered clinical trials - but that does not imply those trials have no value at all.The importance of patients' choices is stressed but the difficulties of making choices and the general poor understanding of risk can make patients and caregivers, as well as healthcare professionals, very vulnerable to making poor decisions. All stakeholders need to be adequately guided through the evidence to make proper informed decisions.

Guidelines for the Content of Statistical Analysis Plans in Clinical Trials.

Importance: While guidance on statistical principles for clinical trials exists, there is an absence of guidance covering the required content of statistical analysis plans (SAPs) to support transparency and reproducibility. Objective: To develop recommendations for a minimum set of items that should be addressed in SAPs for clinical trials, developed with input from statisticians, previous guideline authors, journal editors, regulators, and funders. Design: Funders and regulators (n = 39) of randomized trials were contacted and the literature was searched to identify existing guidance; a survey of current practice was conducted across the network of UK Clinical Research Collaboration-registered trial units (n = 46, 1 unit had 2 responders) and a Delphi survey (n = 73 invited participants) was conducted to establish consensus on SAPs. The Delphi survey was sent to statisticians in trial units who completed the survey of current practice (n = 46), CONSORT (Consolidated Standards of Reporting Trials) and SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) guideline authors (n = 16), pharmaceutical industry statisticians (n = 3), journal editors (n = 9), and regulators (n = 2) (3 participants were included in 2 groups each), culminating in a consensus meeting attended by experts (N = 12) with representatives from each group. The guidance subsequently underwent critical review by statisticians from the surveyed trial units and members of the expert panel of the consensus meeting (N = 51), followed by piloting of the guidance document in the SAPs of 5 trials. Findings: No existing guidance was identified. The registered trials unit survey (46 responses) highlighted diversity in current practice and confirmed support for developing guidance. The Delphi survey (54 of 73, 74% participants completing both rounds) reached consensus on 42% (n = 46) of 110 items. The expert panel (N = 12) agreed that 63 items should be included in the guidance, with an additional 17 items identified as important but may be referenced elsewhere. Following critical review and piloting, some overlapping items were combined, leaving 55 items. Conclusions and Relevance: Recommendations are provided for a minimum set of items that should be addressed and included in SAPs for clinical trials. Trial registration, protocols, and statistical analysis plans are critically important in ensuring appropriate reporting of clinical trials.

Determination of the optimal sample size for a clinical trial accounting for the population size.

The problem of choosing a sample size for a clinical trial is a very common one. In some settings, such as rare diseases or other small populations, the large sample sizes usually associated with the standard frequentist approach may be infeasible, suggesting that the sample size chosen should reflect the size of the population under consideration. Incorporation of the population size is possible in a decision-theoretic approach either explicitly by assuming that the population size is fixed and known, or implicitly through geometric discounting of the gain from future patients reflecting the expected population size. This paper develops such approaches. Building on previous work, an asymptotic expression is derived for the sample size for single and two-arm clinical trials in the general case of a clinical trial with a primary endpoint with a distribution of one parameter exponential family form that optimizes a utility function that quantifies the cost and gain per patient as a continuous function of this parameter. It is shown that as the size of the population, N, or expected size, N∗ in the case of geometric discounting, becomes large, the optimal trial size is O(N1/2) or O(N∗1/2). The sample size obtained from the asymptotic expression is also compared with the exact optimal sample size in examples with responses with Bernoulli and Poisson distributions, showing that the asymptotic approximations can also be reasonable in relatively small sample sizes.

The impact of registration of clinical trials units: the UK experience.

BACKGROUND: Over the last decade, the United Kingdom has invested significant resources in its clinical trial infrastructure. Clinical research networks have been formed, and some general oversight functions for clinical research have been centralised. One of the initiatives is a registration programme for Clinical Trials Units involved in the coordination of clinical trials. An international review panel of experts in clinical trials has been convened for three reviews over time, reviewing applications from Clinical Trials Units in the United Kingdom. The process benefited from earlier work by the National Cancer Research Institute that developed accreditation procedures for trials units involved in cancer trials. This article describes the experience with the three reviews of UK Clinical Trials Units which submitted applications. PURPOSE: This article describes the evolution and impact of this registration process from the perspective of the current international review panel members, some of whom have served on all reviews, including two done by the National Cancer Research Institute. PROCESS: Applications for registration were invited from all active, non-commercial Clinical Trials Units in the United Kingdom. The invitations were issued in 2007, 2009 and 2012, and applicants were asked to describe their expertise and staffing levels in specific areas. To ensure that the reviews were as objective as possible, a description of expected core competencies was developed and applicants were asked to document their compliance with meeting these. The review panel assessed each Clinical Trials Unit against the competencies. The Clinical Trials Unit registration process has evolved over time with each successive review benefiting from what was learned in earlier ones. RESULTS: The review panel has seen positive changes over time, including an increase in the number of units applying, a greater awareness on the part of host institutions about the trials activity within their organisations, more widespread development of Standard Operating Procedures in key areas and improvements in information technology systems used to host clinical trials databases. Key funders are awarding funds only to registered units, and host institutions are implementing procedures and structures to ensure improved communication between all parties involved in trials within their organisation. CONCLUSION: The registration process developed in the United Kingdom has helped to ensure that trials units in the United Kingdom are compliant with regulatory standards and can meet acceptable standards of quality in their conduct of clinical trials. There is an increased awareness among funders, host institutions and Clinical Trials Units themselves of the required competencies, and communication between all those involved in trials has increased. The registration process is an effective and financially viable way of ensuring that objective standards are met at a national level.

Sharing clinical trial data on patient level: opportunities and challenges.

In recent months one of the most controversially discussed topics among regulatory agencies, the pharmaceutical industry, journal editors, and academia has been the sharing of patient-level clinical trial data. Several projects have been started such as the European Medicines Agency´s (EMA) "proactive publication of clinical trial data", the BMJ open data campaign, or the AllTrials initiative. The executive director of the EMA, Dr. Guido Rasi, has recently announced that clinical trial data on patient level will be published from 2014 onwards (although it has since been delayed). The EMA draft policy on proactive access to clinical trial data was published at the end of June 2013 and open for public consultation until the end of September 2013. These initiatives will change the landscape of drug development and publication of medical research. They provide unprecedented opportunities for research and research synthesis, but pose new challenges for regulatory authorities, sponsors, scientific journals, and the public. Besides these general aspects, data sharing also entails intricate biostatistical questions such as problems of multiplicity. An important issue in this respect is the interpretation of multiple statistical analyses, both prospective and retrospective. Expertise in biostatistics is needed to assess the interpretation of such multiple analyses, for example, in the context of regulatory decision-making by optimizing procedural guidance and sophisticated analysis methods.

Ensuring Stakeholder Feedback in the Design and Conduct of Clinical Trials for Rare Diseases: ISCTM Position Paper of the Orphan Disease Working Group.

The 1983 Orphan Drug Act in the United States (US) changed the landscape for development of therapeutics for rare or orphan diseases, which collectively affect approximately 300 million people worldwide, half of whom are children. The act has undoubtedly accelerated drug development for orphan diseases, with over 6,400 orphan drug applications submitted to the US Food and Drug Administration (FDA) from 1983 to 2023, including 350 drugs approved for over 420 indications. Drug development in this population is a global and collaborative endeavor. This position paper of the International Society for Central Nervous System Clinical Trials and Methodology (ISCTM) describes some potential best practices for the involvement of key stakeholder feedback in the drug development process. Stakeholders include advocacy groups, patients and caregivers with lived experience, public and private research institutions (including academia and pharmaceutical companies), treating clinicians, and funders (including the government and independent foundations). The authors articulate the challenges of drug development in orphan diseases and propose methods to address them. Challenges range from the poor understanding of disease history to development of endpoints, targets, and clinical trials designs, to finding solutions to competing research priorities by involved parties.

Structured approach to the elicitation of expert beliefs for a Bayesian-designed clinical trial: a case study.

To quantify uncertainty in a formal manner, statisticians play a vital role in identifying a prior distribution for a Bayesian-designed clinical trial. However, when expert beliefs are to be used to form the prior, the literature is sparse on how feasible and how reliable it is to elicit beliefs from experts. For late-stage clinical trials, high importance is placed on reliability; however, feasibility may be equally important in early-stage trials. This article describes a case study to assess how feasible it is to conduct an elicitation session in a structured manner and to form a probability distribution that would be used in a hypothetical early-stage trial. The case study revealed that by using a structured approach to planning, training and conduct, it is feasible to elicit expert beliefs and form a probability distribution in a timely manner. We argue that by further increasing the published accounts of elicitation of expert beliefs in drug development, there will be increased confidence in the feasibility of conducting elicitation sessions. Furthermore, this will lead to wider dissemination of the pertinent issues on how to quantify uncertainty to both practicing statisticians and others involved with designing trials in a Bayesian manner.

Hurricane risk assessment in a multi-hazard context for Dominica in the Caribbean.

Hurricanes can trigger widespread landslides and flooding creating compound hazards and multiple risks for vulnerable populations. An example is the island of Dominica in the Caribbean, where the population lives predominantly along the coast close to sea level and is subject to storm surge, with steep topography rising behind, with a propensity for landslides and flash river flooding. The simultaneous occurrence of the multiple hazards amplifies their impacts and couples with physical and social vulnerabilities to threaten lives, livelihoods, and the environment. Neglecting compound hazards underestimates overall risk. Using a whole island macroscale, (level-I) analysis, susceptibility scenarios for hurricanes, triggered landslides, and floods were developed by incorporating physical process parameters. The susceptibilities were combined with vulnerability indicators to map spatial patterns of hurricane multi-risks in Dominica. The analysis adopted a coupled approach involving the frequency ratio (FR), analytic hierarchy process (AHP), and geographic information system (GIS). Detailed hazard modelling was done at selected sites (level-II), incorporating storm surge estimates, landslide runout simulations, and steady flow analysis for floods. High-resolution terrain data and simulation models, the Rapid Mass Movement Simulation (RAMMS) and the hydrologic engineering center's river analysis system (HEC-RAS), were employed. Ground validation confirmed reasonable agreement between projected and observed scenarios across different spatial scales. Following the United Nations Office for disaster risk reduction (UNDRR) call for the inclusion of local, traditional, and indigenous knowledge, feedback, and expert opinion to improve understanding of disaster risk, 17 interviews with local experts and 4 participatory workshops with residents were conducted, and findings were incorporated into the analysis, so as to gain insights into risk perceptions. The study's outcomes encompass projections and quantification of hurricane compound hazards, vulnerabilities, accumulated risks, and an understanding of local priorities. These findings will inform decision-making processes for risk mitigation choices and community actions by providing a new framework for multi-hazard risk assessment that is easy to implement in combining different data forms.

Patient Centricity: Design and Conduct of Clinical Trials in Orphan Diseases: Third of Three Sets of Expanded Proceedings from the 2020 ISCTM Autumn Conference on Pediatric Drug Development.

This article expands on a session, titled "Patient Centricity: Design and Conduct of Clinical Trials in Orphan Diseases," that was presented as part of a two-day meeting on Pediatric Drug Development at the International Society for Central Nervous System (CNS) Clinical Trials and Methodology (ISCTM) Autumn Conference in Boston, Massachusetts, in October 2020. Speakers from various areas of pediatric drug development addressed a variety of implications of including children in drug development programs, including implications for rare/orphan diseases. The speakers have written summaries of their talks. The session's lead Chair was Dr. Joan Busner, who wrote introductory and closing comments. Dr. Simon Day, regulatory consultant, outlined some of the past mistakes that have plagued trials that did not consult with patient groups in the early design phase. Dr. Atul Mahableshwarkar provided an industry perspective of a recent trial that benefited from the inclusion of patient input. Drs. Lucas Kempf and Maria Sheean provided regulatory input from the perspectives of the United States (US) Food and Drug Administration (FDA) and European Medicines Agency (EMA), respectively. Dr. Judith Dunn outlined a novel approach for assessing and rank ordering patient and clinician clinical meaningfulness and the disconnect that may occur. Dr. Busner provided closing comments, tied together the presented issues, and provided a synopsis of the lively discussion that followed the session. In addition to the speakers above, the discussion included two representatives from patient advocacy groups, as well as an additional speaker who described the challenges of conducting a pediatric trial in the US and European Union (EU), given the often competing regulatory requirements. This article should serve as an expert-informed reference to those interested and involved in CNS drug development programs that are aimed at children and rare diseases and seek to ensure a patient-centric approach.

Appropriate design and reporting of superiority, equivalence and non-inferiority clinical trials incorporating a benefit-risk assessment: the BRAINS study including expert workshop.

Background: Randomised controlled trials are designed to assess the superiority, equivalence or non-inferiority of a new health technology, but which trial design should be used is not always obvious in practice. In particular, when using equivalence or non-inferiority designs, multiple outcomes of interest may be important for the success of a trial, despite the fact that usually only a single primary outcome is used to design the trial. Benefit-risk methods are used in the regulatory clinical trial setting to assess multiple outcomes and consider the trade-off of the benefits against the risks, but are not regularly implemented in publicly funded trials. Objectives: The aim of the project is to aid the design of clinical trials with multiple outcomes of interest by defining when each trial design is appropriate to use and identifying when to use benefit-risk methods to assess outcome trade-offs (qualitatively or quantitatively) in a publicly funded trial setting. Methods: A range of methods was used to elicit expert opinion to answer the project objectives, including a web-based survey of relevant researchers, a rapid review of current literature and a 2-day consensus workshop of experts (in 2019). Results: We created a list of 19 factors to aid researchers in selecting the most appropriate trial design, containing the following overarching sections: population, intervention, comparator, outcomes, feasibility and perspectives. Six key reasons that indicate a benefit-risk method should be considered within a trial were identified: (1) when the success of the trial depends on more than one outcome; (2) when important outcomes within the trial are in competing directions (i.e. a health technology is better for one outcome, but worse for another); (3) to allow patient preferences to be included and directly influence trial results; (4) to provide transparency on subjective recommendations from a trial; (5) to provide consistency in the approach to presenting results from a trial; and (6) to synthesise multiple outcomes into a single metric. Further information was provided to support the use of benefit-risk methods in appropriate circumstances, including the following: methods identified from the review were collated into different groupings and described to aid the selection of a method; potential implementation of methods throughout the trial process were provided and discussed (with examples); and general considerations were described for those using benefit-risk methods. Finally, a checklist of five pieces of information that should be present when reporting benefit-risk methods was defined, with two additional items specifically for reporting the results. Conclusions: These recommendations will assist research teams in selecting which trial design to use and deciding whether or not a benefit-risk method could be included to ensure research questions are answered appropriately. Additional information is provided to support consistent use and clear reporting of benefit-risk methods in the future. The recommendations can also be used by funding committees to confirm that appropriate considerations of the trial design have been made. Limitations: This research was limited in scope and should be considered in conjunction with other trial design methodologies to assess appropriateness. In addition, further research is needed to provide concrete information about which benefit-risk methods are best to use in publicly funded trials, along with recommendations that are specific to each method. Study registration: The rapid review is registered as PROSPERO CRD42019144882. Funding: Funded by the Medical Research Council UK and the National Institute for Health and Care Research as part of the Medical Research Council-National Institute for Health and Care Research Methodology Research programme.

Randomised controlled trials are considered the best way to gather evidence about potential NHS treatments. They can be designed from different perspectives depending whether the aim is to show that a new treatment is better than, equal to or no worse than the current best available treatment. The selection of this design relates to the single most important outcome; however, often multiple outcomes can be affected by a treatment. For example, a new treatment may improve disease management but increase side effects. Patients want a treatment to work but not at the price of poor quality of life; therefore, a trade-off must be made, and the recommended treatment depends on this trade-off. Benefit­risk methods can assess the trade-off between multiple outcomes and can include patient preference. These methods could improve the way that decisions are made about treatments in the NHS, but there is currently limited research about the use of these methods in publicly funded trials. The aim of this report is to improve the design of clinical trials by helping researchers to select the most appropriate trial design and to decide when to include a benefit­risk method. The recommendations were created using the opinions of experts within the field and consisted of a survey, review of the literature and a workshop. The project created a list of 19 factors that can assist researchers to select the most appropriate trial design. Furthermore, six key areas were identified in which researchers may consider including a benefit­risk method within a trial. Finally, if a benefit­risk assessment is being used, a checklist of items has been created that identifies the information important to include in reports. This report is, however, limited in its applicability and further research should extend this work, as well as provide more detail on individual methods that are available.