Cluster Randomised Trials

Hayes, Richard; Moulton, Lawrence H.

doi:10.1201/9781584888178

Cited by 711 publications

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“…Differences in study outcomes between Battlemind and the standard brief were analyzed using mixed-effects models to take account of possible cluster effects (Hayes & Moulton, 2009;RabeHesketh & Skrondal, 2008). A two-level analysis was used, with individual nested within company.…”

Section: Discussionmentioning

confidence: 99%

Postdeployment Battlemind training for the U.K. armed forces: A cluster randomized controlled trial.

Mulligan

Fear

Jones

et al. 2012

Journal of Consulting and Clinical Psychology

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Objective: Combat exposure can increase the risk of subsequent psychological ill-health in armed forces (AF) personnel. A U.S. postdeployment psycho-educational intervention, Battlemind, showed a beneficial effect on mental health in U.S. military personnel exposed to high combat levels.We evaluated the effectiveness of an anglicized version of postdeployment Battlemind. Method:Battlemind was adapted for the United Kingdom. The main amendments were to sections about carrying weapons, driving, and alcohol misuse. The anglicized Battlemind was compared with the U.K. standard postdeployment brief in a cluster randomized controlled trial. At baseline, 2,443 U.K. AF personnel returning from Afghanistan via Cyprus completed questionnaires about their combat experiences and mental health. Of these, 1,616 (66%) completed 6-month follow-up questionnaires. We used the Posttraumatic Stress Disorder Checklist (PCL-C) to measure probable posttraumatic stress disorder and the General Health Questionnaire (GHQ-12) to measure common mental disorders. Secondary outcomes included alcohol misuse, assessed with the Alcohol Use Disorders Identification Test (AUDIT), and binge drinking. Mixed-effects models were used to account for possible cluster effects. Results:We did not find a difference in mental health or overall AUDIT score. Those who received Battlemind versus the standard brief were less likely to report binge drinking, although the effect size was small (adjusted odds ratio = 0.73, 95% CI [0.58, 0.92]). Conclusions:The anglicized Battlemind did not improve mental health but had a modest impact on the reporting of binge drinking. Alcohol misuse is problematic in military populations; therefore, an intervention that reduces binge drinking may be helpful.

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Section: Discussionmentioning

confidence: 99%

Postdeployment Battlemind training for the U.K. armed forces: A cluster randomized controlled trial.

Mulligan

Fear

Jones

et al. 2012

Journal of Consulting and Clinical Psychology

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“…As an alternative to blocking, some advocate rerandomization when a given randomization results in poor balance in observed covariates (26,27). Rerandomization restricts the randomization scheme, as assignments with poor balance are ruled out.…”

Section: Discussionmentioning

confidence: 99%

Improving massive experiments with threshold blocking

Higgins

Sävje

Sekhon

2016

Proc. Natl. Acad. Sci. U.S.A.

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Inferences from randomized experiments can be improved by blocking: assigning treatment in fixed proportions within groups of similar units. However, the use of the method is limited by the difficulty in deriving these groups. Current blocking methods are restricted to special cases or run in exponential time; are not sensitive to clustering of data points; and are often heuristic, providing an unsatisfactory solution in many common instances. We present an algorithm that implements a widely applicable class of blocking-threshold blockingthat solves these problems. Given a minimum required group size and a distance metric, we study the blocking problem of minimizing the maximum distance between any two units within the same group. We prove this is a nondeterministic polynomial-time hard problem and derive an approximation algorithm that yields a blocking where the maximum distance is guaranteed to be, at most, four times the optimal value. This algorithm runs in O(n log n) time with O(n) space complexity. This makes it, to our knowledge, the first blocking method with an ensured level of performance that works in massive experiments. Whereas many commonly used algorithms form pairs of units, our algorithm constructs the groups flexibly for any chosen minimum size. This facilitates complex experiments with several treatment arms and clustered data. A simulation study demonstrates the efficiency and efficacy of the algorithm; tens of millions of units can be blocked using a desktop computer in a few minutes.experimental design | blocking | big data | causal inference

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“…The communities are represented with a stochastic block network model (9), in which contacts between individuals within the same block are far more common than those between blocks. This assumption is essential as it increases the strength of indirect effects within clusters relative to scenarios in which there is more between-cluster transmission (10). A connection between individuals in the network represents a single infectious…”

Section: Simulated Population Structurementioning

confidence: 99%

Competing Effects of Indirect Protection and Clustering on the Power of Cluster-Randomized Controlled Vaccine Trials

Hitchings

Lipsitch

Wang

et al. 2018

American Journal of Epidemiology

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Power considerations for trials evaluating vaccines against infectious diseases are complicated by indirect protective effects of vaccination. While cluster-randomized trials (cRCTs) are less statistically efficient than individually randomized trials (iRCT), a cRCT's ability to measure direct and indirect vaccine effects may mitigate the loss of efficiency due to clustering. Within cRCTs, the number and size of clusters affects three determinants of power: the effect size being measured, disease incidence, and intra-cluster correlation. We simulate trials conducted in a collection of small communities to assess how indirect protection and clustering affect the power of cRCTs and iRCTs during an emerging epidemic. Across diverse parameters, we find that within the same trial population, cRCTs are never more powerful than iRCTs, although the difference can be small. We also identify two effects that attenuate the loss of cRCT power traditionally associated with increased cluster size. First, if enrollment of fewer, larger clusters is performed to achieve higher vaccine coverage within vaccinated communities, this increases the effect to be measured and, consequently, power. Second, the greater rate of imported transmission in larger communities may increase the attack rate and similarly mitigate loss of power relative to a trial in many, smaller communities. Cluster-randomized controlled trials (cRCTs) have become an increasingly common method for evaluating interventions for infectious diseases, including vaccines. Compared to individually randomized controlled trials (iRCTs), cRCTs may offer logistical, operational, and acceptability advantages (1), and allow the measurement of direct and indirect effects of vaccination, which are often relevant for policy-makers (2). The statistical theory of cRCT design has largely focused on the effect of clustering, commonly measured by intracluster correlation, on power (3-5). Intracluster correlation arises because outcomes of members of the same cluster are more similar than those from different clusters. Therefore, increasing the number of individuals within a cluster provides less information than would adding the same number of individuals in a new cluster.When the trial outcome is an infectious disease, correlation arises also because each case in a cluster can transmit infection to other cluster members. Thus, trials of vaccines against infectious diseases exhibit a more complicated relationship between statistical power and sample size than in trials for non-infectious outcomes (6, 7); in particular, the total or overall vaccine effect measured by a cRCT is generally larger than the direct effect measured by an iRCT. In principle, this increased effect size in a cRCT might partially or fully offset the loss of power due to within-cluster correlation. Understanding these complexities can aid in vaccine trial design for emerging epidemics. While an important consideration in any clinical trial, maximizing efficiency is particularly crucial in trials during ...

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Cluster Randomised Trials

Cited by 711 publications

References 0 publications

Postdeployment Battlemind training for the U.K. armed forces: A cluster randomized controlled trial.

Postdeployment Battlemind training for the U.K. armed forces: A cluster randomized controlled trial.

Improving massive experiments with threshold blocking

Competing Effects of Indirect Protection and Clustering on the Power of Cluster-Randomized Controlled Vaccine Trials

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