Can emergency medicine research benefit from adaptive design clinical trials?

Background: Adaptive design clinical trials use preplanned interim analyses to determine whether studies should be stopped or modified before recruitment is complete. Emergency medicine trials are well suited to these designs as many have a short time to primary outcome relative to the length of recruitment. We hypothesised that the majority of published emergency medicine trials have the potential to use a simple adaptive trial design. Methods: We reviewed clinical trials published in three emergency medicine journals between January 2003 and December 2013. We determined the proportion that used an adaptive design as well as the proportion that could have used a simple adaptive design based on the time to primary outcome and length of recruitment. Results: Only 19 of 188 trials included in the review were considered to have used an adaptive trial design. A total of 154/165 trials that were fixed in design had the potential to use an adaptive design. Conclusions: Currently, there seems to be limited uptake in the use of adaptive trial designs in emergency medicine despite their potential benefits to save time and resources. Failing to take advantage of adaptive designs could be costly to patients and research. It is recommended that where practical and logistical considerations allow, adaptive designs should be used for all emergency medicine clinical trials

Tutorial in biostatistics - Sample sizes for clinical trials with Normal data

This article gives an overview of sample size calculations for parallel group and cross‐over studies with Normal data. Sample size derivation is given for trials where the objective is to demonstrate: superiority, equivalence, non‐inferiority, bioequivalence and estimation to a given precision, for different types I and II errors. It is demonstrated how the different trial objectives influence the null and alternative hypotheses of the trials and how these hypotheses influence the calculations. Sample size tables for the different types of trials and worked examples are given

Guidance for using pilot studies to inform the design of intervention trials with continuous outcomes

Background: A pilot study can be an important step in the assessment of an intervention by providing information to design the future definitive trial. Pilot studies can be used to estimate the recruitment and retention rates and population variance and to provide preliminary evidence of efficacy potential. However, estimation is poor because pilot studies are small, so sensitivity analyses for the main trial's sample size calculations should be undertaken. Methods: We demonstrate how to carry out easy-to-perform sensitivity analysis for designing trials based on pilot data using an example. Furthermore, we introduce rules of thumb for the size of the pilot study so that the overall sample size, for both pilot and main trials, is minimized. Results: The example illustrates how sample size estimates for the main trial can alter dramatically by plausibly varying assumptions. Required sample size for 90% power varied from 392 to 692 depending on assumptions. Some scenarios were not feasible based on the pilot study recruitment and retention rates. Conclusion: Pilot studies can be used to help design the main trial, but caution should be exercised. We recommend the use of sensitivity analyses to assess the robustness of the design assumptions for a main trial

Are pilot trials useful for predicting randomisation and attrition rates in definitive studies: A review of publicly funded trials

BACKGROUND/AIMS: External pilot trials are recommended for testing the feasibility of main or confirmatory trials. However, there is little evidence that progress in external pilot trials actually predicts randomisation and attrition rates in the main trial. To assess the use of external pilot trials in trial design, we compared randomisation and attrition rates in publicly funded randomised controlled trials with rates in their pilots. METHODS: Randomised controlled trials for which there was an external pilot trial were identified from reports published between 2004 and 2013 in the Health Technology Assessment Journal. Data were extracted from published papers, protocols and reports. Bland-Altman plots and descriptive statistics were used to investigate the agreement of randomisation and attrition rates between the full and external pilot trials. RESULTS: Of 561 reports, 41 were randomised controlled trials with pilot trials and 16 met criteria for a pilot trial with sufficient data. Mean attrition and randomisation rates were 21.1% and 50.4%, respectively, in the pilot trials and 16.8% and 65.2% in the main. There was minimal bias in the pilot trial when predicting the main trial attrition and randomisation rate. However, the variation was large: the mean difference in the attrition rate between the pilot and main trial was -4.4% with limits of agreement of -37.1% to 28.2%. Limits of agreement for randomisation rates were -47.8% to 77.5%. CONCLUSION: Results from external pilot trials to estimate randomisation and attrition rates should be used with caution as comparison of the difference in the rates between pilots and their associated full trial demonstrates high variability. We suggest using internal pilot trials wherever appropriate

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. CP curves for accruing outcome information were calculated using and compared with a pre-specified objective criteria for original and transformed versions of the trial data using four future treatment effect assumptions: (i) observed current trend, (ii) hypothesised effect, (iii) 80% optimistic confidence limit, (iv) 90% optimistic confidence limit. Results The hypothesised effect assumption met objective criteria when the true effect was close to that planned, but not when smaller than planned. The opposite was seen using the current trend assumption. Optimistic confidence limit assumptions appeared to offer a compromise between the two, performing well against objective criteria when the end observed effect was as planned or smaller. Conclusion The current trend assumption could be the preferable assumption when there is a wish to stop early for futility. Interim analyses could be undertaken as early as 30% of patients have data available. Optimistic confidence limit assumptions should be considered when using CP to make trial decisions, although later interim timings should be considered where logistically feasible

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. It found that a simple letter sent from the family doctor during the school holidays to a parent with a child with asthma, informing them of the importance of taking asthma preventer medication during the summer relatively increased prescriptions by 30% in August and reduced medical contacts in the period September to December. Also, it is estimated there was a cost-saving of £36.07 per patient over the year. We aim to conduct a randomised trial to assess if informing GP practices of an evidence-based intervention improves the implementation of that intervention. Methods/design The TRAINS study—TRial to Assess Implementation of New research in a primary care Setting—is a pragmatic cluster randomised implementation trial using routine data. A total of 1389 general practitioner (GP) practices in England will be included into the trial; 694 GP practices will be randomised to the intervention group and 695 control group of usual care. The Clinical Practice Research Datalink (CPRD) will send the intervention and obtain all data for the study, including prescription and primary care contacts data. The intervention will be sent in June 2021 by postal and email to the asthma lead and/or practice manager. The intervention is a letter to GPs informing them of the PLEASANT study findings with recommendations. It will come with an information leaflet about PLEASANT and a suggested reminder letter and SMS text template. Discussion The trial will assess if informing GP practices of the PLEASANT trial results will increase prescription uptake before the start of the school year. The hope is that the intervention will increase the implementation of PLEASANT work and then increase prescription uptake during the summer holiday prior to the start of school

Open-label, cluster randomised controlled trial and economic evaluation of a brief letter from a GP on unscheduled medical contacts associated with the start of the school year: the PLEASANT trial

BACKGROUND: Asthma is seasonal with peaks in exacerbation rates in school-age children associated with the return to school following the summer vacation. A drop in prescription collection in August is associated with an increase in the number of unscheduled contacts after the school return. OBJECTIVE: To assess whether a public health intervention delivered in general practice reduced unscheduled medical contacts in children with asthma. DESIGN: Cluster randomised trial with trial-based economic evaluation. Randomisation was at general practice level, stratified by size of practice. The intervention group received a letter from their general practitioner (GP) in late July outlining the importance of (re)taking asthma medication before the return to school. The control group was usual care. SETTING: General practices in England and Wales. PARTICIPANTS: 12 179 school-age children in 142 general practices (70 randomised to intervention). MAIN OUTCOME: Proportion of children aged 5-16 years who had an unscheduled contact in September. Secondary endpoints included collection of prescriptions in August and medical contacts over 12 months (September-August). Economic endpoints were quality-adjusted life-years gained and health service costs. RESULTS: There was no evidence of effect (OR 1.09; 95% CI 0.96 to 1.25 against treatment) on unscheduled contacts in September. The intervention increased the proportion of children collecting a prescription in August by 4% (OR 1.43; 95% CI 1.24 to 1.64). The intervention also reduced the total number of medical contacts between September-August by 5% (incidence ratio 0.95; 95% CI 0.91 to 0.99).The mean reduction in medical contacts informed the health economics analyses. The intervention was estimated to save £36.07 per patient, with a high probability (96.3%) of being cost-saving. CONCLUSIONS: The intervention succeeded in increasing children collecting prescriptions. It did not reduce unscheduled care in September (the primary outcome), but in the year following the intervention, it reduced the total number of medical contacts. TRIAL REGISTRATION NUMBER: ISRCTN03000938; Results

Preventing and Lessening Exacerbations of Asthma in School-aged children Associated with a New Term (PLEASANT): Recruiting Primary Care Research Sites-the PLEASANT experience

Background: Recruitment of general practices and their patients into research studies is frequently reported as a challenge. The Preventing and Lessening Exacerbations of Asthma in School-aged children Associated with a New Term (PLEASANT) trial recruited 142 general practices, across England and Wales and delivered the study intervention to time and target. Aims: To describe the process of recruitment used within the cluster randomised PLEASANT trial and present results on factors that influenced recruitment. Methods: Data were collected on the number of and types of contact used to gain expression of interest and subsequent randomisation into the PLEASANT trial. Practice size and previous research experience were also collected. Results: The mean number of contacts required to gain expression of interest were m=3.01 (s.d. 1.6) and total number of contacts from initial invitation to randomisation m=6.8 (s.d. 3.5). Previous randomised controlled trial involvement (hazard ratio (HR)=1.81 (confidence interval (CI) 95%, 1.55–2.11) P<0.001) and number of studies a practice had previously engaged in (odds ratio (OR) 1.91 (CI 95%, (1.52–2.42)) P<0.001), significantly influenced whether a practice would participate in PLEASANT. Practice size was not a significant deciding factor (OR=1.04 (95% CI 0.99–1.08) P=0.137). Conclusions: Recruitment to time and target can be achieved in general practice. The amount of resource required for site recruitment should not, however, be underestimated and multiple strategies for contacting practices should be considered. General practitioners with more research experience are more likely to participate in studies