Bayesian adaptive clinical trial designs for respiratory medicine.

The use of Bayesian adaptive designs for clinical trials has increased in recent years, particularly during the COVID-19 pandemic. Bayesian adaptive designs offer a flexible and efficient framework for conducting clinical trials and may provide results that are more useful and natural to interpret for clinicians, compared to traditional approaches. In this review, we provide an introduction to Bayesian adaptive designs and discuss its use in recent clinical trials conducted in respiratory medicine. We illustrate this approach by constructing a Bayesian adaptive design for a multi-arm trial that compares two non-invasive ventilation treatments to standard oxygen therapy for patients with acute cardiogenic pulmonary oedema. We highlight the benefits and some of the challenges involved in designing and implementing Bayesian adaptive trials.

Repeated Transcranial Magnetic Stimulation for Improving Cognition in Alzheimer Disease: Protocol for an Interim Analysis of a Randomized Controlled Trial.

BACKGROUND: Many clinical trials investigating treatment efficacy require an interim analysis. Recently we have been running a large, multisite, randomized, placebo-controlled, double-blind clinical trial investigating the effect of repetitive transcranial magnetic stimulation (rTMS) treatment for improving or stabilizing the cognition of patients diagnosed with Alzheimer disease. OBJECTIVE: The objectives of this paper are to report on recruitment, adherence, and adverse events (AEs) to date, and to describe in detail the protocol for interim analysis of the clinical trial data. The protocol will investigate whether the trial is likely to reach its objectives if continued to the planned maximum sample size. METHODS: The specific requirements of the analytic protocol are to (1) ensure the double-blind nature of the data while doing the analysis, (2) estimate the predictive probabilities of success (PPoSs), (3) estimate the numbers needed to treat, (4) re-estimate the initial required sample size. The initial estimate of sample size was 208. The interim analysis will be based on 150 patients who will be enrolled in the study and finish at least 8 weeks of the study. Our protocol for interim analysis, at the very first stage, is to determine the response rate for each participant to the treatment (either sham or active), while ensuring the double-blind nature of the data. The blinded data will be analyzed by a statistician to investigate the treatment efficacy. We will use Bayesian PPoS to predict the success rate and determine whether the study should continue. RESULTS: The enrollment has been slowed significantly due to the COVID-19 pandemic and lockdown. Nevertheless, so far 133 participants have been enrolled, while 22 of these have been withdrawn or dropped out for various reasons. In general, rTMS has been found tolerable with no serious AE. Only 2 patients dropped out of the study due to their intolerability to rTMS pulses. CONCLUSIONS: Overall, the study with the same protocol is going as expected with no serious AE or any major protocol deviation. TRIAL REGISTRATION: ClinicalTrials.gov NCT02908815; https://clinicaltrials.gov/ct2/show/NCT02908815. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/31183.

Statistical design of Phase II/III clinical trials for testing therapeutic interventions in COVID-19 patients.

BACKGROUND: Because of unknown features of the COVID-19 and the complexity of the population affected, standard clinical trial designs on treatments may not be optimal in such patients. We propose two independent clinical trials designs based on careful grouping of patient and outcome measures. METHODS: Using the World Health Organization ordinal scale on patient status, we classify treatable patients (Stages 3-7) into two risk groups. Patients in Stages 3, 4 and 5 are categorized as the intermediate-risk group, while patients in Stages 6 and 7 are categorized as the high-risk group. To ensure that an intervention, if deemed efficacious, is promptly made available to vulnerable patients, we propose a group sequential design incorporating four factors stratification, two interim analyses, and a toxicity monitoring rule for the intermediate-risk group. The primary response variable (binary variable) is based on the proportion of patients discharged from hospital by the 15th day. The goal is to detect a significant improvement in this response rate. For the high-risk group, we propose a group sequential design incorporating three factors stratification, and two interim analyses, with no toxicity monitoring. The primary response variable for this design is 30 day mortality, with the goal of detecting a meaningful reduction in mortality rate. RESULTS: Required sample size and toxicity boundaries are calculated for each scenario. Sample size requirements for designs with interim analyses are marginally greater than ones without. In addition, for both the intermediate-risk group and the high-risk group, the required sample size with two interim analyses is almost identical to analyses with just one interim analysis. CONCLUSIONS: We recommend using a binary outcome with composite endpoints for patients in Stage 3, 4 or 5 with a power of 90% to detect an improvement of 20% in the response rate, and a 30 day mortality rate outcome for those in Stage 6 or 7 with a power of 90% to detect 15% (effect size) reduction in mortality rate. For the intermediate-risk group, two interim analyses for efficacy evaluation along with toxicity monitoring are encouraged. For the high-risk group, two interim analyses without toxicity monitoring is advised.

Clinical Trials Impacted by the COVID-19 Pandemic: Adaptive Designs to the Rescue?

Very recently the new pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified and the coronavirus disease 2019 (COVID-19) declared a pandemic by the World Health Organization. The pandemic has a number of consequences for ongoing clinical trials in non-COVID-19 conditions. Motivated by four current clinical trials in a variety of disease areas we illustrate the challenges faced by the pandemic and sketch out possible solutions including adaptive designs. Guidance is provided on (i) where blinded adaptations can help; (ii) how to achieve Type I error rate control, if required; (iii) how to deal with potential treatment effect heterogeneity; (iv) how to use early read-outs; and (v) how to use Bayesian techniques. In more detail approaches to resizing a trial affected by the pandemic are developed including considerations to stop a trial early, the use of group-sequential designs or sample size adjustment. All methods considered are implemented in a freely available R shiny app. Furthermore, regulatory and operational issues including the role of data monitoring committees are discussed.