Reporting of cluster randomised crossover trials: extension of the CONSORT 2010 statement with explanation and elaboration.

This article presents the CONSORT (consolidated standards of reporting trials) extension for cluster randomised crossover trials. A cluster randomised crossover trial involves randomisation of groups of individuals (known as clusters) to different sequences of interventions over time. The design has gained popularity in settings where cluster randomisation is required because it can largely overcome the loss in power due to clustering in parallel cluster trials.

A simple and effective method for simulating nested exchangeable correlated binary data for longitudinal cluster randomised trials.

Background: Simulation is an important tool for assessing the performance of statistical methods for the analysis of data and for the planning of studies. While methods are available for the simulation of correlated binary random variables, all have significant practical limitations for simulating outcomes from longitudinal cluster randomised trial designs, such as the cluster randomised crossover and the stepped wedge trial designs. For these trial designs as the number of observations in each cluster increases these methods either become computationally infeasible or their range of allowable correlations rapidly shrinks to zero.

The impact of dung beetles on the free-living stages of ruminant parasites in faeces and their role as biological control agents in grazing livestock.

Dung beetles provide a variety of ecosystem services in both natural and farmed landscapes. Amongst these services, reductions in the abundance of the free-living stages of pests and parasites that develop in faeces is considered to be of great importance. There is evidence from Australia that enhanced dung beetle populations can reduce populations of pest fly species, particularly the bush fly, however, there is little empirical evidence for reductions in the incidence and impact of nematode parasitism in grazing ruminants.

Same-visit hepatitis C testing and treatment to accelerate cure among people who inject drugs (the QuickStart Study): a cluster randomised cross-over trial protocol.

Introduction: Despite universal access to government-funded direct-acting antivirals (DAAs) in 2016, the rate of hepatitis C treatment uptake in Australia has declined substantially. Most hepatitis C is related to injecting drug use; reducing the hepatitis C burden among people who inject drugs (PWID) is, therefore, paramount to reach hepatitis C elimination targets. Increasing DAA uptake by PWID is important for interrupting transmission and reducing incidence, as well as reducing morbidity and mortality and improving quality of life of PWID and meeting Australia's hepatitis C elimination targets.

Maintaining the validity of inference from linear mixed models in stepped-wedge cluster randomized trials under misspecified random-effects structures.

Linear mixed models are commonly used in analyzing stepped-wedge cluster randomized trials. A key consideration for analyzing a stepped-wedge cluster randomized trial is accounting for the potentially complex correlation structure, which can be achieved by specifying random-effects. The simplest random effects structure is random intercept but more complex structures such as random cluster-by-period, discrete-time decay, and more recently, the random intervention structure, have been proposed.

Advancing randomized controlled trial methodologies: The place of innovative trial design in eating disorders research.

Randomized controlled trials can be used to generate evidence on the efficacy and safety of new treatments in eating disorders research. Many of the trials previously conducted in this area have been deemed to be of low quality, in part due to a number of practical constraints. This article provides an overview of established and more innovative clinical trial designs, accompanied by pertinent examples, to highlight how design choices can enhance flexibility and improve efficiency of both resource allocation and participant involvement.

Comparison of statistical methods used to meta-analyse results from interrupted time series studies: an empirical study.

Background: The Interrupted Time Series (ITS) is a robust design for evaluating public health and policy interventions or exposures when randomisation may be infeasible. Several statistical methods are available for the analysis and meta-analysis of ITS studies. We sought to empirically compare available methods when applied to real-world ITS data.

The staircase cluster randomised trial design: A pragmatic alternative to the stepped wedge.

This article introduces the 'staircase' design, derived from the zigzag pattern of steps along the diagonal of a stepped wedge design schematic where clusters switch from control to intervention conditions. Unlike a complete stepped wedge design where all participating clusters must collect and provide data for the entire trial duration, clusters in a staircase design are only required to be involved and collect data for a limited number of pre- and post-switch periods. This could alleviate some of the burden on participating clusters, encouraging involvement in the trial and reducing the likelihood of attrition.

Evaluation of statistical methods used to meta-analyse results from interrupted time series studies: A simulation study.

Interrupted time series (ITS) are often meta-analysed to inform public health and policy decisions but examination of the statistical methods for ITS analysis and meta-analysis in this context is limited. We simulated meta-analyses of ITS studies with continuous outcome data, analysed the studies using segmented linear regression with two estimation methods [ordinary least squares (OLS) and restricted maximum likelihood (REML)], and meta-analysed the immediate level- and slope-change effect estimates using fixed-effect and (multiple) random-effects meta-analysis methods. Simulation design parameters included varying series length; magnitude of lag-1 autocorrelation; magnitude of level- and slope-changes; number of included studies; and, effect size heterogeneity.

Generalizing the information content for stepped wedge designs: A marginal modeling approach.

Stepped wedge trials are increasingly adopted because practical constraints necessitate staggered roll-out. While a complete design requires clusters to collect data in all periods, resource and patient-centered considerations may call for an incomplete stepped wedge design to minimize data collection burden. To study incomplete designs, we expand the metric of information content to discrete outcomes.

Does it decay? Obtaining decaying correlation parameter values from previously analysed cluster randomised trials.

A frequently applied assumption in the analysis of data from cluster randomised trials is that the outcomes from all participants within a cluster are equally correlated. That is, the intracluster correlation, which describes the degree of dependence between outcomes from participants in the same cluster, is the same for each pair of participants in a cluster. However, recent work has discussed the importance of allowing for this correlation to decay as the time between the measurement of participants in a cluster increases.

The impact of iterative removal of low-information cluster-period cells from a stepped wedge design.

Background: Standard stepped wedge trials, where clusters switch from the control to the intervention condition in a staggered manner, can be costly and burdensome. Recent work has shown that the amount of information contributed by each cluster in each period differs, with some cluster-periods contributing a relatively small amount of information. We investigate the patterns of the information content of cluster-period cells upon iterative removal of low-information cells, assuming a model for continuous outcomes with constant cluster-period size, categorical time period effects, and exchangeable and discrete-time decay intracluster correlation structures.

Sample size for partially nested designs and other nested or crossed designs with a continuous outcome when adjusted for baseline.

In a randomized controlled trial, outcomes of different subjects may be independent at baseline, but correlated at a follow-up measurement due to treatment. This treatment-related clustering at follow-up can arise for instance because the treatment is given in a group or because subjects are treated individually but by the same therapist (therapist effect). There is substantial literature on the design and analysis of such trials when estimation of the intervention effect is based on a follow-up measurement (eg, directly after treatment or at a later time point).

Prehospital Tranexamic Acid for Severe Trauma.

Background: Whether prehospital administration of tranexamic acid increases the likelihood of survival with a favorable functional outcome among patients with major trauma and suspected trauma-induced coagulopathy who are being treated in advanced trauma systems is uncertain.

Methods: We randomly assigned adults with major trauma who were at risk for trauma-induced coagulopathy to receive tranexamic acid (administered intravenously as a bolus dose of 1 g before hospital admission, followed by a 1-g infusion over a period of 8 hours after arrival at the hospital) or matched placebo. The primary outcome was survival with a favorable functional outcome at 6 months after injury, as assessed with the use of the Glasgow Outcome Scale-Extended (GOS-E).

Effect estimates can be accurately calculated with data digitally extracted from interrupted time series graphs.

Interrupted time series (ITS) studies are frequently used to examine the impact of population-level interventions or exposures. Systematic reviews with meta-analyses including ITS designs may inform public health and policy decision-making. Re-analysis of ITS may be required for inclusion in meta-analysis.

Mental health and wellbeing of health and aged care workers in Australia, May 2021 - June 2022: a longitudinal cohort study.

Objectives: To assess the mental health and wellbeing of health and aged care workers in Australia during the second and third years of the coronavirus disease 2019 (COVID-19) pandemic, overall and by occupation group.

Design, Setting, Participants: Longitudinal cohort study of health and aged care workers (ambulance, hospitals, primary care, residential aged care) in Victoria: May-July 2021 (survey 1), October-December 2021 (survey 2), and May-June 2022 (survey 3).

Main Outcome Measures: Proportions of respondents (adjusted for age, gender, socio-economic status) reporting moderate to severe symptoms of depression (Patient Health Questionnaire-9, PHQ-9), anxiety (Generalized Anxiety Disorder scale, GAD-7), or post-traumatic stress (Impact of Event Scale-6, IES-6), burnout (abbreviated Maslach Burnout Inventory, aMBI), or high optimism (10-point visual analogue scale); mean scores (adjusted for age, gender, socio-economic status) for wellbeing (Personal Wellbeing Index-Adult, PWI-A) and resilience (Connor Davidson Resilience Scale 2, CD-RISC-2).

Effect of atorvastatin on skeletal muscles of patients with knee osteoarthritis: analysis of a randomised controlled trial.

Objective: Populations with knee osteoarthritis (KOA) are at increased risk of cardiovascular disease, due to higher prevalence of risk factors including dyslipidaemia, where statins are commonly prescribed. However, the effect of statins on muscles and symptoms in this population is unknown. Thus, this study examined the effect of atorvastatin on muscle properties in patients with symptomatic KOA.

The batched stepped wedge design: A design robust to delays in cluster recruitment.

Stepped wedge designs are an increasingly popular variant of longitudinal cluster randomized trial designs, and roll out interventions across clusters in a randomized, but step-wise fashion. In the standard stepped wedge design, assumptions regarding the effect of time on outcomes may require that all clusters start and end trial participation at the same time. This would require ethics approvals and data collection procedures to be in place in all clusters before a stepped wedge trial can start in any cluster.

Evaluating the performance of Bayesian and restricted maximum likelihood estimation for stepped wedge cluster randomized trials with a small number of clusters.

Background: Stepped wedge trials are an appealing and potentially powerful cluster randomized trial design. However, they are frequently implemented with a small number of clusters. Standard analysis methods for these trials such as a linear mixed model with estimation via maximum likelihood or restricted maximum likelihood (REML) rely on asymptotic properties and have been shown to yield inflated type I error when applied to studies with a small number of clusters.

Methodological systematic review recommends improvements to conduct and reporting when meta-analyzing interrupted time series studies.

Objectives: Interrupted Time Series (ITS) are a type of nonrandomized design commonly used to evaluate public health policy interventions, and the impact of exposures, at the population level. Meta-analysis may be used to combine results from ITS across studies (in the context of systematic reviews) or across sites within the same study. We aimed to examine the statistical approaches, methods, and completeness of reporting in reviews that meta-analyze results from ITS.

Is antibiotic treatment effective in the management of chronic low back pain with disc herniation? Study protocol for a randomised controlled trial.

Background: There has been immense interest and debate regarding the effectiveness of antibiotic treatment for chronic low back pain. Two randomised controlled trials have examined the efficacy of antibiotics for chronic low back pain with disc herniation and Modic changes, but have reported conflicting results. The aim of this double-blind, randomised, placebo-controlled trial is to determine the efficacy of antibiotic treatment in a broader patient subgroup of chronic low back pain with disc herniation and investigate whether the presence of Modic changes predicts response to antibiotic therapy.

Inference for the treatment effect in longitudinal cluster randomized trials when treatment effect heterogeneity is ignored.

In cluster-randomized trials, sometimes the effect of the intervention being studied differs between clusters, commonly referred to as treatment effect heterogeneity. In the analysis of stepped wedge and cluster-randomized crossover trials, it is possible to include terms in outcome regression models to allow for such treatment effect heterogeneity yet this is not frequently considered. Outside of some simulation studies of specific cases where the outcome is binary, the impact of failing to include terms for treatment effect heterogeneity on the variance of the treatment effect estimator is unknown.

Evaluation of statistical methods used in the analysis of interrupted time series studies: a simulation study.

Background: Interrupted time series (ITS) studies are frequently used to evaluate the effects of population-level interventions or exposures. However, examination of the performance of statistical methods for this design has received relatively little attention.

Methods: We simulated continuous data to compare the performance of a set of statistical methods under a range of scenarios which included different level and slope changes, varying lengths of series and magnitudes of lag-1 autocorrelation.

Completeness of reporting and risks of overstating impact in cluster randomised trials: a systematic review.

Overstating the impact of interventions through incomplete or inaccurate reporting can lead to inappropriate scale-up of interventions with low impact. Accurate reporting of the impact of interventions is of great importance in global health research to protect scarce resources. In global health, the cluster randomised trial design is commonly used to evaluate complex, multicomponent interventions, and outcomes are often binary.

Comparison of six statistical methods for interrupted time series studies: empirical evaluation of 190 published series.

Background: The Interrupted Time Series (ITS) is a quasi-experimental design commonly used in public health to evaluate the impact of interventions or exposures. Multiple statistical methods are available to analyse data from ITS studies, but no empirical investigation has examined how the different methods compare when applied to real-world datasets.

Methods: A random sample of 200 ITS studies identified in a previous methods review were included.