Evaluation of a policy intervention to promote the health and wellbeing of workers in small and medium sized enterprises – a cluster randomised controlled trial

Kudrna, Laura

doi:10.1186/s12889-019-6582-y

Cited by 8 publications

(22 citation statements)

References 10 publications

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“…While Bayesian methods are relatively rarely used for the design and analysis of cluster randomised trials, there are growing number of examples (e.g. at Work Wellbeing Programme Collaboration 52 ). Chaloner 53 provides a review of Bayesian optimal experimental design criteria.…”

Section: Discussionmentioning

confidence: 99%

Optimal study designs for cluster randomised trials: An overview of methods and results

Watson,

Girling,

Hemming

2023

Stat Methods Med Res

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There are multiple possible cluster randomised trial designs that vary in when the clusters cross between control and intervention states, when observations are made within clusters, and how many observations are made at each time point. Identifying the most efficient study design is complex though, owing to the correlation between observations within clusters and over time. In this article, we present a review of statistical and computational methods for identifying optimal cluster randomised trial designs. We also adapt methods from the experimental design literature for experimental designs with correlated observations to the cluster trial context. We identify three broad classes of methods: using exact formulae for the treatment effect estimator variance for specific models to derive algorithms or weights for cluster sequences; generalised methods for estimating weights for experimental units; and, combinatorial optimisation algorithms to select an optimal subset of experimental units. We also discuss methods for rounding experimental weights, extensions to non-Gaussian models, and robust optimality. We present results from multiple cluster trial examples that compare the different methods, including determination of the optimal allocation of clusters across a set of cluster sequences and selecting the optimal number of single observations to make in each cluster-period for both Gaussian and non-Gaussian models, and including exchangeable and exponential decay covariance structures.

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Section: Discussionmentioning

confidence: 99%

Optimal study designs for cluster randomised trials: An overview of methods and results

Watson,

Girling,

Hemming

2023

Stat Methods Med Res

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“…Up to 15 employees were sampled and interviewed from each cluster. The full protocol is published elsewhere 8 (at the time of writing the results from the trial are under review).…”

Section: Applied Examplementioning

confidence: 99%

“…Often these interventions target “higher level” processes and can be complex in nature. 5 , 6 , 7 Recent examples from our own work include an incentive scheme to improve implementation of a broad package of education and activities designed to improve employee health in the workplace, 8 or a community health worker program targeting multiple health conditions. 9 The effects of such complex interventions cannot be adequately summarized by a single outcome.…”

Section: Introductionmentioning

confidence: 99%

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Permutation‐based multiple testing corrections for P$$ P $$‐values and confidence intervals for cluster randomized trials

Watson

Akinyemi

Hemming

2023

Statistics in Medicine

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In this article, we derive and compare methods to derive P‐values and sets of confidence intervals with strong control of the family‐wise error rates and coverage for estimates of treatment effects in cluster randomized trials with multiple outcomes. There are few methods for P‐value corrections and deriving confidence intervals, limiting their application in this setting. We discuss the methods of Bonferroni, Holm, and Romano and Wolf and adapt them to cluster randomized trial inference using permutation‐based methods with different test statistics. We develop a novel search procedure for confidence set limits using permutation tests to produce a set of confidence intervals under each method of correction. We conduct a simulation‐based study to compare family‐wise error rates, coverage of confidence sets, and the efficiency of each procedure in comparison to no correction using both model‐based standard errors and permutation tests. We show that the Romano‐Wolf type procedure has nominal error rates and coverage under non‐independent correlation structures and is more efficient than the other methods in a simulation‐based study. We also compare results from the analysis of a real‐world trial.

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“…The roots of well-being research are anchored in ancient Greece, where philosophers focused on how to achieve "the good life, " which we currently call well-being (Stoll, 2014). Since then, the search for happiness or the meaning of life has been a constant topic handled by different disciplines; today, we have diverse scientific evidence demonstrating that "well-being" has a direct impact on people's health (Hajek and Helmut König, 2019;Van de Cauter et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Social Impact of Psychological Research on Well-Being Shared in Social Media

et al. 2020

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The purpose of this article is to demonstrate how the Social Impact in Social Media (SISM, hereinafter) methodology applied in psychological research provides evidence for the visibility of the social impact of the research. This article helps researchers become aware of whether and how their improvements are capturing the interest of citizens and how citizens are applying such evidence and obtaining better outcomes, in this case, in relation to well-being. In addition, citizens can access the latest evidence on social media and act as channels of communication between science and social or personal networks and, in doing so, they can improve the living conditions of others. This methodology is also useful for agencies that support researchers in psychology with financial assistance, which can use it to evaluate the social impact of the funds that they invest in research. In this article, the 10 studies on well-being were selected for analysis using the following criteria: their research results led to demonstrable improvement in well-being, and these improvements are presented on social media. We applied the social impact coverage ratio to identify the percentage of the social impact shared in social media in relation to the total amount of social media data collected. Finally, examples of quantitative and qualitative evidence of the social impact of the research on well-being are presented.

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Evaluation of a policy intervention to promote the health and wellbeing of workers in small and medium sized enterprises – a cluster randomised controlled trial

Cited by 8 publications

References 10 publications

Optimal study designs for cluster randomised trials: An overview of methods and results

Optimal study designs for cluster randomised trials: An overview of methods and results

Permutation‐based multiple testing corrections for P$$ P $$‐values and confidence intervals for cluster randomized trials

Social Impact of Psychological Research on Well-Being Shared in Social Media

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