Clinical trials are challenging in rare diseases like pediatric cancers, where the accrual is limited. In these trials, inference assumptions are the same as in common diseases, that is the sample comes from a quasi-infinite population. This leads to overestimating the variance of the mean treatment effect. The finite-population correction factor correcting this bias is often used in surveys, but not in clinical trials. With few assumptions, the use of this correction factor can improve trials efficiency, showing that the power of those trials is sometimes higher than it appears. First, a simulation study assesses the standard error of the mean treatment effect and coverage of the 95% confidence interval with and without the correction. Second, the analytical power of a z-test with and without the correction is given. Finally, the impact on the sample size calculation is investigated. The impact of assuming a finite population is assessed for varying treatment effect, sample size and population size. The simulation results confirm the overestimation of the standard error without the correction factor. When using the correction factor, the gain in power reaches up to 10.1%, 15.3% and 12.3% to detect a difference in treatment effect of 10%, 15% and 20%, respectively. The gain is negligible for n = 30, in scenarios with high power (>95%), and for large populations. This gain in power translates into a decrease in sample size: if the conventional calculation leads to a sample size 10% of the population size, then the sample size can be divided by 1.1; if the conventional calculation leads to a sample size 20% of the population size, then the sample size can be divided by 1.2, in order to reach the planned type I error and power. When dealing with rare diseases like pediatric cancers, the power of clinical trials might be higher than it appears if using conventional sample sizes. When correcting the variance of the mean using the population size, a gain in efficiency is observed with reasonable sample sizes and treatment differences for very small population sizes, showing that this approach can be useful for some pediatric cancer clinical trials.

Laryngotracheal stenosis is a rare but devastating proximal airway fibrosis that restricts a patient's ability to breathe. Treatment is primarily surgical and to date, there has never been a multi-institutional, randomized, prospective, and interventional clinical trial for a medical therapy to treat laryngotracheal stenosis. Therefore, we aimed to obtain patient feedback to guide successful trial design, recruitment, retention, and for identifying potential barriers to study participation. Over 1000 members of an international laryngotracheal stenosis online support community (the Living with Idiopathic Subglottic Stenosis Facebook group) were sent two questionnaires for a proposed interventional double-blinded, randomized, placebo-controlled clinical trial. A total of 317 and 558 participants responded to the first and second surveys, respectively. The majority of participants (77%) were willing to consider enrollment, regardless of having a 50% chance of receiving placebo versus treatment (78%). The majority (84%) of participants were willing to travel 200 miles to participate for up to six in-person visits over 50 days. Specific side effects, including anemia/thrombocytopenia (72%) or risk of infection (69.3%) had the greatest impact on clinical trial participation with other side effects (peripheral edema (53%), oral ulcers (51%), and gastrointestinal side effects (41%)) having less impact. Patients with laryngotracheal stenosis possess nuanced insight into their disease and treatment options. As a group, they are extremely motivated for better therapies. Future laryngotracheal stenosis clinical trials should focus on providing excellent side effect -related education and utilizing feedback from online advocacy groups to optimize recruitment and retention.

We need more pragmatic trials of interventions to improve care and outcomes for people living with Alzheimer's disease and related dementias. However, these trials present unique methodological challenges in their design, analysis, and reporting-often, due to the presence of one or more sources of clustering. Failure to account for clustering in the design and analysis can lead to increased risks of Type I and Type II errors. We conducted a review to describe key methodological characteristics and obtain a "baseline assessment" of methodological quality of pragmatic trials in dementia research, with a view to developing new methods and practical guidance to support investigators and methodologists conducting pragmatic trials in this field. We used a published search filter in MEDLINE to identify trials more likely to be pragmatic and identified a subset that focused on people living with Alzheimer's disease or other dementias or included them as a defined subgroup. Pairs of reviewers extracted descriptive information and key methodological quality indicators from each trial. We identified N = 62 eligible primary trial reports published across 36 different journals. There were 15 (24%) individually randomized, 38 (61%) cluster randomized, and 9 (15%) individually randomized group treatment designs; 54 (87%) trials used repeated measures on the same individual and/or cluster over time and 17 (27%) had a multivariate primary outcome (e.g. due to measuring an outcome on both the patient and their caregiver). Of the 38 cluster randomized trials, 16 (42%) did not report sample size calculations accounting for the intracluster correlation and 13 (34%) did not account for intracluster correlation in the analysis. Of the 9 individually randomized group treatment trials, 6 (67%) did not report sample size calculations accounting for intracluster correlation and 8 (89%) did not account for it in the analysis. Of the 54 trials with repeated measurements, 45 (83%) did not report sample size calculations accounting for repeated measurements and 19 (35%) did not utilize at least some of the repeated measures in the analysis. No trials accounted for the multivariate nature of their primary outcomes in sample size calculation; only one did so in the analysis. There is a need and opportunity to improve the design, analysis, and reporting of pragmatic trials in dementia research. Investigators should pay attention to the potential presence of one or more sources of clustering. While methods for longitudinal and cluster randomized trials are well developed, accessible resources and new methods for dealing with multiple sources of clustering are required. Involvement of a statistician with expertise in longitudinal and clustered designs is recommended.

Introduction:Feasibility evaluations are performed to create the best possible starting point for the set-up and execution of a clinical trial, and to identify any obstacles for successful trial conduct. New digital technologies can provide various types of data for use in feasibility evaluations. There is a need to identify and compare such data sources for trial site identification and for evaluating the sites’ patient recruitment potential. Especially, information is needed on the use of electronic health records. We investigated how different data sources are used by pharmaceutical companies operating in the Nordic countries for identifying trial sites and for evaluating their potential to recruit trial participants.Methods:This was a semi-structured qualitative interview study with 21 participants from pharmaceutical companies and contract research organizations operating in Finland, Sweden, Denmark and Norway. Qualitative content analysis was applied.Results:For identifying countries and trial sites on a global level, the trial sponsors mostly used databases on previous trial performance. The use of electronic health record data was very limited. Sites’ and investigators’ visibility in various databases was seen as fundamental for their countries becoming selected into new clinical trials. For estimating the sites’ recruitment projections, most sites were seen to base their patient count estimates solely on their previous experience. Some sites had reviewed their electronic health record data, which was considered to increase the accuracy of their recruitment estimates and these sites’ attractivity. Along with dialogs with investigators, the sponsors used various data sources to validate the investigators’ estimates. Legislative obstacles were seen to hinder the use of electronic health record queries for estimation of patient counts.Conclusion:Visibility in the databases used by trial sponsors is crucial for the countries and sites to be identified. Site selection appears to be based on trust and relationships built from experience, but electronic data provide the support upon which the trust is based. Estimation of the number of potential trial participants is a complex and time-consuming process for both investigators and sponsors. Sponsors seem to favour sites who could support their patient count estimates with electronic health record data as they were quicker in providing the estimates and more reliable than sites with no electronic health record evidence. The patient count evaluation process could be simplified, accelerated and made more reliable with more systematic use of electronic health record evidence in the feasibility evaluation phase. This would increase the accuracy of the patient count estimates and, on its part, contribute to improved recruitment success.

Cluster-randomized trials allow for the evaluation of a community-level or group-/cluster-level intervention. For studies that require a cluster-randomized trial design to evaluate cluster-level interventions aimed at controlling vector-borne diseases, it may be difficult to assess a large number of clusters while performing the additional work needed to monitor participants, vectors, and environmental factors associated with the disease. One such example of a cluster-randomized trial with few clusters was the "efficacy and risk of harms of repeated ivermectin mass drug administrations for control of malaria" trial. Although previous work has provided recommendations for analyzing trials like repeated ivermectin mass drug administrations for control of malaria, additional evaluation of the multiple approaches for analysis is needed for study designs with count outcomes. Using a simulation study, we applied three analysis frameworks to three cluster-randomized trial designs (single-year, 2-year parallel, and 2-year crossover) in the context of a 2-year parallel follow-up of repeated ivermectin mass drug administrations for control of malaria. Mixed-effects models, generalized estimating equations, and cluster-level analyses were evaluated. Additional 2-year parallel designs with different numbers of clusters and different cluster correlations were also explored. Mixed-effects models with a small sample correction and unweighted cluster-level summaries yielded both high power and control of the Type I error rate. Generalized estimating equation approaches that utilized small sample corrections controlled the Type I error rate but did not confer greater power when compared to a mixed model approach with small sample correction. The crossover design generally yielded higher power relative to the parallel equivalent. Differences in power between analysis methods became less pronounced as the number of clusters increased. The strength of within-cluster correlation impacted the relative differences in power. Regardless of study design, cluster-level analyses as well as individual-level analyses like mixed-effects models or generalized estimating equations with small sample size corrections can both provide reliable results in small cluster settings. For 2-year parallel follow-up of repeated ivermectin mass drug administrations for control of malaria, we recommend a mixed-effects model with a pseudo-likelihood approximation method and Kenward-Roger correction. Similarly designed studies with small sample sizes and count outcomes should consider adjustments for small sample sizes when using a mixed-effects model or generalized estimating equation for analysis. Although the 2-year parallel follow-up of repeated ivermectin mass drug administrations for control of malaria is already underway as a parallel trial, applying the simulation parameters to a crossover design yielded improved power, suggesting that crossover designs may be valuable in settings where the number of available clusters is limited. Finally, the sensitivity of the analysis approach to the strength of within-cluster correlation should be carefully considered when selecting the primary analysis for a cluster-randomized trial.

Current research largely tends to ignore the drug-testing model that was developed in the "Second World" as an explicit alternative to the randomized controlled trial. This system can be described as "socialist pharmapolitics," accounting for the specific features of state socialism that influenced the development and testing of experimental drugs. The clinical trials model employed in the "Second World" was heavily influenced by the Soviet Union, which was by far the most influential player in the socialist bloc during the Cold War. Based on extensive archival research, this article presents an empirical case of a late Soviet clinical trial as a pragmatic alternative to the randomized controlled trial model. It accounts for the divergences between the official model prescribed by the Soviet authorities and the messy realities of healthcare practice. It further outlines different factors that ultimately shaped how clinical trials were organized in Soviet institutions "on the ground." Accordingly, this article presents a "real-life" history of "socialist pharmapolitics" and outlines the problems that this system faced in practice. Archival research was conducted at the Russian State Archive of Scientific and Technical Documentation in Moscow. Archival files include scientific, technical, and registration documentation such as biochemical, pharmacological, and clinical descriptions of the experimental drug Meldonium, letters between various hospitals, research institutes and the Soviet regulatory body, as well as 26 reports of completed clinical trials. Manual content analysis was used for the interpretation of results. This article presents an empirical case of a late Soviet clinical trial as a pragmatic alternative to the randomized controlled trial model. It demonstrates some key differences from the randomized controlled trial model. This article also highlights some of the discrepancies between the model that was officially prescribed by the Soviet authorities and the realities of experimental drug testing in the Soviet Union in the late 1980s and early 1990s. In particular, it notes some elements of randomization, double-blinding, and the use of placebo that were present in Meldonium trials despite being formally denounced by Soviet bioethics. The Soviet model for testing experimental drugs differed from the Western one substantially in a number of respects. This difference was not only proclaimed officially by the Soviet authorities, but was for the most part enforced in clinical trials in practice. At the same time, our research demonstrates that there were important differences between the official model and the clinical realities on the ground.

Blinding aims to minimize biases from what participants and investigators know or believe. Randomized controlled trials, despite being the gold standard to evaluate treatment effect, do not generally assess the success of blinding. We investigated the extent of blinding in back pain trials and the associations between participant guesses and treatment effects. We did a review with PubMed/OvidMedline, 2000-2019. Eligibility criteria were back pain trials with data available on treatment effect and participants' guess of treatment. For blinding, blinding index was used as chance-corrected measure of excessive correct guess (0 for random guess). For treatment effects, within- or between-arm effect sizes were used. Analyses of investigators' guess/blinding or by treatment modality were performed exploratorily. Forty trials (3899 participants) were included. Active and sham treatment groups had mean blinding index of 0.26 (95% confidence interval: 0.12, 0.41) and 0.01 (-0.11, 0.14), respectively, meaning 26% of participants in active treatment believed they received active treatment, whereas only 1% in sham believed they received sham treatment, beyond chance, that is, random guess. A greater belief of receiving active treatment was associated with a larger within-arm effect size in both arms, and ideal blinding (namely, "random guess," and "wishful thinking" that signifies both groups believing they received active treatment) showed smaller effect sizes, with correlation of effect size and summary blinding indexes of 0.35 (p = 0.028) for between-arm comparison. We observed uniformly large sham treatment effects for all modalities, and larger correlation for investigator's (un)blinding, 0.53 (p = 0.046). Participants in active treatments in back pain trials guessed treatment identity more correctly, while those in sham treatments tended to display successful blinding. Excessive correct guesses (that could reflect weaker blinding and/or noticeable effects) by participants and investigators demonstrated larger effect sizes. Blinding and sham treatment effects on back pain need due consideration in individual trials and meta-analyses.

Combinations of treatments that have already received regulatory approval can offer additional benefit over Each of the treatments individually. However, trials of these combinations are lower priority than those that develop novel therapies, which can restrict funding, timelines and patient availability. This article develops a novel trial design to facilitate the evaluation of New combination therapies. This trial design combines elements of phase II and phase III trials to reduce the burden of evaluating combination therapies, while also maintaining a feasible sample size. This design was developed for a randomised trial that compares the properties of three combination doses of ketamine and dexmedetomidine, given intranasally, to ketamine delivered intravenously for children undergoing a closed reduction for a fracture or dislocation. This trial design uses response-adaptive randomisation to evaluate different dose combinations and increase the information collected for successful novel drug combinations. The design then uses Bayesian dose-response modelling to undertake a comparative effectiveness analysis for the most successful dose combination against a relevant comparator. We used simulation methods determine the thresholds for adapting the trial and making conclusions. We also used simulations to evaluate the probability of selecting the dose combination with the highest true effectiveness the operating characteristics of the design and its Bayesian predictive power. With 410 participants, five interim updates of the randomisation ratio and a probability of effectiveness of 0.93, 0.88 and 0.83 for the three dose combinations, we have an 83% chance of randomising the largest number of patients to the drug with the highest probability of effectiveness. Based on this adaptive randomisation procedure, the comparative effectiveness analysis has a type I error of less than 5% and a 93% chance of correcting concluding non-inferiority, when the probability of effectiveness for the optimal combination therapy is 0.9. In this case, the trial has a greater than 77% chance of meeting its dual aims of dose-finding and comparative effectiveness. Finally, the Bayesian predictive power of the trial is over 90%. By simultaneously determining the optimal dose and collecting data on the relative effectiveness of an intervention, we can minimise administrative burden and recruitment time for a trial. This will minimise the time required to get effective, safe combination therapies to patients quickly. The proposed trial has high potential to meet the dual study objectives within a feasible overall sample size.

There has been a good deal of discussion in the literature regarding which subjects are vulnerable in the context of clinical trials. There has been significantly less discussion regarding when and how to include vulnerable subjects in clinical trials. This lack of guidance is a particular problem for trials covered by the US regulations, which mandate strict requirements on the inclusion of three groups: pregnant women/fetuses, prisoners, and children. For the past 30 years, funders, investigators, and institutional review boards have frequently responded to these regulations by excluding pregnant women/fetuses, prisoners, and children from clinical trials. More recent work has emphasized the extent to which a default of exclusion can undermine the value of clinical trials, especially pragmatic trials. A default of exclusion also has the potential to undermine the interests of vulnerable groups, in both the short and the long term. These concerns raise the need for guidance on how to satisfy existing US regulations, while minimizing their negative impact on the value of clinical trials and the interests of vulnerable groups. The present manuscript thus describes a six-step decision procedure that institutional review boards can use to determine when and how to include vulnerable subjects in clinical trials, including pragmatic trials, that are covered by US regulations.