Mesenchymal intravenous stromal cell infusions in children with recessive dystrophic epidermolysis bullosa: MissionEB protocol for a randomised, double-blinded, placebo-controlled, two-centre, crossover trial with an internal phase I dose de-escalation phase and open-label extension.

Introduction: Recessive dystrophic epidermolysis bullosa (RDEB) is a severe genetic mucocutaneous fragility disorder characterised by chronic blistering, slow wound healing and increased risk of squamous cell carcinoma. Current management options are very limited.

Methods: This is a randomised (1:1), placebo-controlled, double-blinded crossover (A/B) trial with an internal phase I dose de-escalation (4+5 design) in the first 3 months and a 12-month continued treatment follow-on open-label study if 3-month outcome data from the crossover trial indicate safe and beneficial effects.

Analysis of recurrent events in cluster randomised trials: The PLEASANT trial case study.

Recurrent events for many clinical conditions, such as asthma, can indicate poor health outcomes. Recurrent events data are often analysed using statistical methods such as Cox regression or negative binomial regression, suffering event or time information loss. This article re-analyses the preventing and lessening exacerbations of asthma in school-age children associated with a new term (PLEASANT) trial data as a case study, investigating the utility, extending recurrent events survival analysis methods to cluster randomised trials.

Reporting and communication of sample size calculations in adaptive clinical trials: a review of trial protocols and grant applications.

Background: An adaptive design allows modifying the design based on accumulated data while maintaining trial validity and integrity. The final sample size may be unknown when designing an adaptive trial. It is therefore important to consider what sample size is used in the planning of the study and how that is communicated to add transparency to the understanding of the trial design and facilitate robust planning.

Assurance methods for designing a clinical trial with a delayed treatment effect.

An assurance calculation is a Bayesian alternative to a power calculation. One may be performed to aid the planning of a clinical trial, specifically setting the sample size or to support decisions about whether or not to perform a study. Immuno-oncology is a rapidly evolving area in the development of anticancer drugs.

Evaluation of the impact of letters to GP practices to promote asthma prescription uptake in school-age children during summer (TRAINS study): a pragmatic cluster-randomised controlled trial.

Background: Asthma exacerbations peak in school-aged children after the return to school in September. Previous studies have shown a decline in collections of asthma prescriptions during August. The PLEASANT trial demonstrated that sending a reminder letter to parents increased prescription uptake; reduced unscheduled care, and was cost saving to the health service.

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.

A retrospective analysis of conditional power assumptions in clinical trials with continuous or binary endpoints.

Background: Adaptive clinical trials may use conditional power (CP) to make decisions at interim analyses, requiring assumptions about the treatment effect for remaining patients. It is critical that these assumptions are understood by those using CP in decision-making, as well as timings of these decisions.

Methods: Data for 21 outcomes from 14 published clinical trials were made available for re-analysis.

Developing guidance for a risk-proportionate approach to blinding statisticians within clinical trials: a mixed methods study.

Background: Existing guidelines recommend statisticians remain blinded to treatment allocation prior to the final analysis and that any interim analyses should be conducted by a separate team from the one undertaking the final analysis. However, there remains substantial variation in practice between UK Clinical Trials Units (CTUs) when it comes to blinding statisticians. Therefore, the aim of this study was to develop guidance to advise CTUs on a risk-proportionate approach to blinding statisticians within clinical trials.

TRial to Assess Implementation of New research in a primary care Setting (TRAINS): study protocol for a pragmatic cluster randomised controlled trial of an educational intervention to promote asthma prescription uptake in general practitioner practices.

Background: There is a marked increase in unscheduled care visits in school-aged children with asthma after returning to school in September. This is potentially associated with children not taking their asthma preventer medication during the school summer holidays. A cluster randomised controlled trial (PLEASANT) was undertaken with 1279 school-age children in 141 general practices (71 on intervention and 70 on control) in England and Wales.

Optimal pharmacotherapy pathway in adults with diabetic peripheral neuropathic pain: the OPTION-DM RCT.

Background: The mainstay of treatment for diabetic peripheral neuropathic pain is pharmacotherapy, but the current National Institute for Health and Care Excellence guideline is not based on robust evidence, as the treatments and their combinations have not been directly compared.

Objectives: To determine the most clinically beneficial, cost-effective and tolerated treatment pathway for diabetic peripheral neuropathic pain.

Design: A randomised crossover trial with health economic analysis.

An investigation of the constancy of effect in Cochrane systematic reviews in context with the assumptions for noninferiority trials.

Unlabelled: When designing a noninferiority (NI) study one of the most important steps is to set the noninferiority (NI) limit. The NI limit is an acceptable loss of efficacy for a new investigative treatment compared to an active control treatment - often standard care. The limit should be a value so small that the loss efficacy is clinically zero.

A practical guide to sample size calculations: Installation of the app SampSize.

In 2016 we published three articles in Pharmaceutical Statistics that gave a practical guide to sample size calculations. In each of the articles there were instructions on how to obtain the App SampSize. This short communication updates these instructions and highlights the updates and added functionality to the App.

Adjusting for bias in the mean for primary and secondary outcomes when trials are in sequence.

When designing a clinical trial, one key aspect of the design is the sample size calculation. The sample size calculation tends to rely on a target or expected difference. The expected difference can be based on the observed data from previous studies, which results in bias.

Sample sizes for cluster-randomised trials with continuous outcomes: Accounting for uncertainty in a single intra-cluster correlation estimate.

Sample size calculations for cluster-randomised trials require inclusion of an inflation factor taking into account the intra-cluster correlation coefficient. Often, estimates of the intra-cluster correlation coefficient are taken from pilot trials, which are known to have uncertainty about their estimation. Given that the value of the intra-cluster correlation coefficient has a considerable influence on the calculated sample size for a main trial, the uncertainty in the estimate can have a large impact on the ultimate sample size and consequently, the power of a main trial.

A systematic review of the "promising zone" design.

Introduction: Sample size calculations require assumptions regarding treatment response and variability. Incorrect assumptions can result in under- or overpowered trials, posing ethical concerns. Sample size re-estimation (SSR) methods investigate the validity of these assumptions and increase the sample size if necessary.

The adaptive designs CONSORT extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design.

Adaptive designs (ADs) allow pre-planned changes to an ongoing trial without compromising the validity of conclusions and it is essential to distinguish pre-planned from unplanned changes that may also occur. The reporting of ADs in randomised trials is inconsistent and needs improving. Incompletely reported AD randomised trials are difficult to reproduce and are hard to interpret and synthesise.

Practical help for specifying the target difference in sample size calculations for RCTs: the DELTA five-stage study, including a workshop.

Background: The randomised controlled trial is widely considered to be the gold standard study for comparing the effectiveness of health interventions. Central to its design is a calculation of the number of participants needed (the sample size) for the trial. The sample size is typically calculated by specifying the magnitude of the difference in the primary outcome between the intervention effects for the population of interest.

Correction to: Choosing the target difference and undertaking and reporting the sample size calculation for a randomised controlled trial - the development of the DELTA2 guidance.

Following publication of the original article [1], we have been notified of a few mistakes.

Progression criteria in trials with an internal pilot: an audit of publicly funded randomised controlled trials.

Background: With millions of pounds spent annually on medical research in the UK, it is important that studies are spending funds wisely. Internal pilots offer the chance to stop a trial early if it becomes apparent that the study will not be able to recruit enough patients to show whether an intervention is clinically effective. This study aims to assess the use of internal pilots in individually randomised controlled trials funded by the Health Technology Assessment (HTA) programme and to summarise the progression criteria chosen in these trials.

DELTA guidance on choosing the target difference and undertaking and reporting the sample size calculation for a randomised controlled trial.

Randomised controlled trials are considered to be the best method to assess comparative clinical efficacy and effectiveness, and can be a key source of data for estimating cost effectiveness. Central to the design of a randomised controlled trial is an a priori sample size calculation, which ensures that the study has a high probability of achieving its prespecified main objective. Beyond pure statistical or scientific concerns, it is ethically imperative that an appropriate number of study participants be recruited, to avoid imposing the burdens of a clinical trial on more patients than necessary.

Development process of a consensus-driven CONSORT extension for randomised trials using an adaptive design.

Background: Adequate reporting of adaptive designs (ADs) maximises their potential benefits in the conduct of clinical trials. Transparent reporting can help address some obstacles and concerns relating to the use of ADs. Currently, there are deficiencies in the reporting of AD trials.

Calculation of confidence intervals for a finite population size.

For any estimate of response, confidence intervals are important as they help quantify a plausible range of values for the population response. However, there may be instances in clinical research when the population size is finite, but we wish to take a sample from the population and make inference from this sample. Instances where you can have a fixed population size include when undertaking a clinical audit of patient records or in a clinical trial a researcher could be checking for transcription errors against patient notes.