Interim data monitoring in cluster randomised trials: Practical issues and a case study

Background There is an abundance of guidance for the interim monitoring of individually randomised trials. While methodological literature exists on how to extend these methods to cluster randomised trials, there is little guidance on practical implementation. Cluster trials have many features which make their monitoring needs different. We outline the methodological and practical challenges of interim monitoring of cluster trials; and apply these considerations to a case study. Case study The E-MOTIVE study is an 80-cluster randomised trial of a bundle of interventions to treat postpartum haemorrhage. The proposed data monitoring plan includes (1) monitor sample size assumptions, (2) monitor for evidence of selection bias, and (3) an interim assessment of the primary outcome, as well as monitoring data completeness. The timing of the sample size monitoring is chosen with both consideration of statistical precision and to allow time to recruit more clusters. Monitoring for selection bias involves comparing individual-level characteristics and numbers recruited between study arms to identify any post-randomisation participant identification bias. An interim analysis of outcomes presented with 99.9% confidence intervals using the Haybittle–Peto approach should mitigate any concern regarding the inflation of type-I error. The pragmatic nature of the trial means monitoring for adherence is not relevant, as it is built into a process evaluation. Conclusions The interim analyses of cluster trials have a number of important differences to monitoring individually randomised trials. In cluster trials, there will often be a greater need to monitor nuisance parameters, yet there will often be considerable uncertainty in their estimation. This means the utility of sample size re-estimation can be questionable particularly when there are practical or funding difficulties associated with making any changes to planned sample sizes. Perhaps most importantly interim monitoring has the potential to identify selection bias, particularly in trials with post-randomisation identification or recruitment. Finally, the pragmatic nature of cluster trials might mean that the utility of methods to allow for interim monitoring of outcomes based on statistical testing, or monitoring for adherence to study interventions, are less relevant. Our intention is to facilitate the planning of future cluster randomised trials and to promote discussion and debate to improve monitoring of these studies.

Evaluating the Efficacy of COVID-19 Vaccines

A large number of studies are being conducted to evaluate the efficacy and safety of candidate vaccines against novel coronavirus disease-2019 (COVID-19). Most Phase 3 tri- als have adopted virologically confirmed symptomatic COVID-19 disease as the primary efficacy endpoint, although laboratory-confirmed SARS-CoV-2 is also of interest. In addi- tion, it is important to evaluate the effect of vaccination on disease severity. To provide a full picture of vaccine efficacy and make efficient use of available data, we propose using SARS-CoV-2 infection, symptomatic COVID-19, and severe COVID-19 as dual or triple pri- mary endpoints. We demonstrate the advantages of this strategy through realistic simulation studies. Finally, we show how this approach can provide rigorous interim monitoring of the trials and efficient assessment of the durability of vaccine efficacy.

Continuous research monitoring improves the quality of research conduct and compliance among research trainees: internal evaluation of a monitoring programme

Background: Research site monitoring (RSM) is an effective way to ensure compliance with Good Clinical Practice (GCP). However, RSM is not offered to trainees (investigators) at African Institutions routinely. The Makerere University/Uganda Virus Research Institute Centre of Excellence in Infection and Immunity Research and Training (MUII-Plus) introduced internal monitoring to promote the quality of trainees’ research projects. Here, we share our monitoring model, experiences and achievements, and challenges encountered. Methods: We analysed investigators’ project reports from monitoring visits undertaken from April 2017 to December 2019. Monitors followed a standard checklist to review investigator site files and record forms, and toured site facilities. We planned four monitoring visits for each trainee: one at site initiation, two interim, and a closeout monitoring visit. A team of two monitors conducted the visits. Results: We monitored 25 out of the 26 research projects in progress between April 2017 and December 2019. Compliance with protocols, standard operating procedures, GCP, and GCLP improved with each monitoring visit. Median (IQR) compliance rate was 43% (31%, 44%) at site initiation visit for different monitoring items, 70% (54%, 90%) at the 1st interim monitoring visit, 100% (92%, 100%) at 2nd interim monitoring visit and all projects achieved 100% compliance at site closeout. All investigators had good work ethics and practice, and appropriate facilities. Initially, some investigators’ files lacked essential documents, and informed consent processes needed to be improved. We realized that non-compliant investigators had not received prior training in GCP/GCLP, so we offered them this training. Conclusions: Routine monitoring helps identify non-compliance early and improves the quality of research. We recommend continuous internal monitoring for all research studies. Investigators conducting research involving human subjects should receive GCP/GCLP training before commencing their projects. Institutional higher degrees and research ethics committees should enforce this as a requirement for project approvals.

Evaluating the Efficacy of COVID-19 Vaccines

A large number of studies are being conducted to evaluate the efficacy and safety of candidate vaccines against novel coronavirus disease-2019 (COVID-19). Most Phase 3 trials have adopted virologically confirmed symptomatic COVID-19 disease as the primary efficacy endpoint, although laboratory-confirmed SARS-CoV-2 is also of interest. In addition, it is important to evaluate the effect of vaccination on disease severity. To provide a full picture of vaccine efficacy and make efficient use of available data, we propose using SARS-CoV-2 infection, symptomatic COVID-19, and severe COVID-19 as dual or triple primary endpoints. We demonstrate the advantages of this strategy through realistic simulation studies. Finally, we show how this approach can provide rigorous interim monitoring of the trials and efficient assessment of the durability of vaccine efficacy.SummaryTo increase statistical power and meet vaccine success criteria, we propose to evaluate the efficacy of COVID-19 vaccines by using the dual or triple primary endpoints of SARS-CoV-2 infection, symptomatic COVID-19, and severe COVID-19.

Group sequential design for time-to-event data using the concept of proportional time

Sequential monitoring of efficacy and safety is an important part of clinical trials. A Group Sequential design allows researchers to perform interim monitoring after groups of patients have completed the study. Statistical literature is well developed for continuous and binary outcomes and relies on asymptotic normality of the test statistic. However, in the case of time-to-event data, existing methods of sample size calculation are done either assuming proportional hazards or assuming exponentially distributed lifetimes. In scenarios where these assumptions are not true, as evidenced from historical data, these traditional methods are restrictive and cannot always be used. As interim monitoring is driven by ethical, financial, and administrative considerations, it is imperative that sample size calculations be done in an efficient manner keeping in mind the specific needs of a clinical trial with a time-to-event outcome. To address these issues, a novel group sequential design is proposed using the concept of Proportional Time. This method utilizes the generalized gamma ratio distribution to calculate the efficacy and safety boundaries and can be used for all distributions that are members of the generalized gamma family using an error spending approach. The design incorporates features specific to survival data such as loss to follow-up, administrative censoring, varying accrual times and patterns, binding or non-binding futility rules with or without skips, and flexible alpha and beta spending mechanisms. Three practical examples are discussed, followed by discussion of the important aspects of the proposed design.