Sample size calculation for stepped wedge and other longitudinal cluster randomised trials

Hooper, Richard; Teerenstra, Steven; Hoop, Esther de; Eldridge, Sandra

doi:10.1002/sim.7028

Cited by 187 publications

(330 citation statements)

References 34 publications

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“…This can be incorporated by extending the random-effects components to allow a random interaction between time and cluster v ij :where v ij ~ N (0, σ v 2 ) and where v ij is assumed independent to u i . In this model, each cluster has a different random effect at each time point and thus a different deviation from the average secular trend – although the degree of variation (as indicated by σ v 2 ) is sampled from the same distribution at all time points and for all clusters [24]. It is convenient to think of v ij representing the variation within a cluster due to time varying characteristics of the cluster.…”

Section: Methodsmentioning

confidence: 99%

Analysis of cluster randomised stepped wedge trials with repeated cross-sectional samples

Hemming

Taljaard

Forbes

2017

Trials

134

172

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BackgroundThe stepped wedge cluster randomised trial (SW-CRT) is increasingly being used to evaluate policy or service delivery interventions. However, there is a dearth of trials literature addressing analytical approaches to the SW-CRT. Perhaps as a result, a significant number of published trials have major methodological shortcomings, including failure to adjust for secular trends at the analysis stage. Furthermore, the commonly used analytical framework proposed by Hussey and Hughes makes several assumptions.MethodsWe highlight the assumptions implicit in the basic SW-CRT analytical model proposed by Hussey and Hughes. We consider how simple modifications of the basic model, using both random and fixed effects, can be used to accommodate deviations from the underlying assumptions. We consider the implications of these modifications for the intracluster correlation coefficients. In a case study, the importance of adjusting for the secular trend is illustrated.ResultsThe basic SW-CRT model includes a fixed effect for time, implying a common underlying secular trend across steps and clusters. It also includes a single term for treatment, implying a constant shift in this trend under the treatment. When these assumptions are not realistic, simple modifications can be implemented to allow the secular trend to vary across clusters and the treatment effect to vary across clusters or time. In our case study, the naïve treatment effect estimate (adjusted for clustering but unadjusted for time) suggests a beneficial effect. However, after adjusting for the underlying secular trend, we demonstrate a reversal of the treatment effect.ConclusionDue to the inherent confounding of the treatment effect with time, analysis of a SW-CRT should always account for secular trends or risk-biased estimates of the treatment effect. Furthermore, the basic model proposed by Hussey and Hughes makes a number of important assumptions. Consideration needs to be given to the appropriate model choice at the analysis stage. We provide a Stata code to implement the proposed analyses in the illustrative case study.Electronic supplementary materialThe online version of this article (doi:10.1186/s13063-017-1833-7) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Analysis of cluster randomised stepped wedge trials with repeated cross-sectional samples

Hemming

Taljaard

Forbes

2017

Trials

134

172

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“…The first approach assumes that the correlation between two people from the same cluster is the same whether they are sampled in the same period or a different period 13. The second approach allows the correlation to be weaker between different periods 141516. The distinction is technical but important.…”

Section: Approaches To Analysismentioning

confidence: 99%

Analysis of cluster randomised trials with an assessment of outcome at baseline

Hooper

Forbes

Hemming

et al. 2018

BMJ

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In a cluster randomised clinical trial, all the participants who belong to the same cluster (eg, a local community, school, or general practice) are randomised to receive the same treatment. People from the same cluster tend to be more similar than those from different clusters, and the analysis must allow for this.A common enhancement is to add an assessment of participants’ outcomes in a baseline period—that is, before randomisation. Even if different participants are assessed at baseline and follow-up, the fact that they are sampled from the same cluster allows some control for cluster differences. But the analysis of cluster randomised trials is more complex than for individually randomised trials, and it is not obvious how best to adjust for baseline assessments. A variety of methods of analysis have been suggested: in this article, we describe different approaches and their flaws, and make recommendations.

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“…For example, some trials may involve cross-sectional examination of a population, potentially accruing measurements from an entirely different set of subjects in each time period [10]. At the other end of the scale, they could involve gathering repeated measurements from a single cohort [11]. …”

Section: Introductionmentioning

confidence: 99%

Stepped wedge cluster randomized controlled trial designs: a review of reporting quality and design features

Grayling

Wason

Mander

2017

Trials

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BackgroundThe stepped wedge (SW) cluster randomized controlled trial (CRCT) design is being used with increasing frequency. However, there is limited published research on the quality of reporting of SW-CRCTs. We address this issue by conducting a literature review.MethodsMedline, Ovid, Web of Knowledge, the Cochrane Library, PsycINFO, the ISRCTN registry, and ClinicalTrials.gov were searched to identify investigations employing the SW-CRCT design up to February 2015. For each included completed study, information was extracted on a selection of criteria, based on the CONSORT extension to CRCTs, to assess the quality of reporting.ResultsA total of 123 studies were included in our review, of which 39 were completed trial reports. The standard of reporting of SW-CRCTs varied in quality. The percentage of trials reporting each criterion varied to as low as 15.4%, with a median of 66.7%.ConclusionsThere is much room for improvement in the quality of reporting of SW-CRCTs. This is consistent with recent findings for CRCTs. A CONSORT extension for SW-CRCTs is warranted to standardize the reporting of SW-CRCTs.Electronic supplementary materialThe online version of this article (doi:10.1186/s13063-017-1783-0) contains supplementary material, which is available to authorized users.

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Sample size calculation for stepped wedge and other longitudinal cluster randomised trials

Cited by 187 publications

References 34 publications

Analysis of cluster randomised stepped wedge trials with repeated cross-sectional samples

Analysis of cluster randomised stepped wedge trials with repeated cross-sectional samples

Analysis of cluster randomised trials with an assessment of outcome at baseline

Stepped wedge cluster randomized controlled trial designs: a review of reporting quality and design features

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