期刊
CLINICAL TRIALS
卷 18, 期 5, 页码 529-540出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/17407745211020852
关键词
Intra-cluster correlation coefficient; within-period correlation; between-period correlation; cluster autocorrelation; discrete-time decay; cluster randomised trial; sample size
资金
- Australian Government Research Training Programme (RTP) Scholarship
- UK Medical Research Council (MRC)
- UK Department for International Development (DFID) under the MRC/DFID Concordat agreement
- European Union [MR/R010161/1]
- Monash University Network of Excellence grant
- International Network for Innovative Cluster Randomised Trial Designs [NOE180009]
- National Institute for Health Research (NIHR) [SRF-2017-10-002]
- Forbes AB
- National Institutes of Health Research (NIHR) [SRF-2017-10-002] Funding Source: National Institutes of Health Research (NIHR)
This study provides a repository of intra-cluster correlations and cluster autocorrelations for longitudinal cluster trials, which can help inform sample size calculations for future longitudinal cluster randomised trials.
Background: Sample size calculations for longitudinal cluster randomised trials, such as crossover and stepped-wedge trials, require estimates of the assumed correlation structure. This includes both within-period intra-cluster correlations, which importantly differ from conventional intra-cluster correlations by their dependence on period, and also cluster autocorrelation coefficients to model correlation decay. There are limited resources to inform these estimates. In this article, we provide a repository of correlation estimates from a bank of real-world clustered datasets. These are provided under several assumed correlation structures, namely exchangeable, block-exchangeable and discrete-time decay correlation structures. Methods: Longitudinal studies with clustered outcomes were collected to form the CLustered OUtcome Dataset bank. Forty-four available continuous outcomes from 29 datasets were obtained and analysed using each correlation structure. Patterns of within-period intra-cluster correlation coefficient and cluster autocorrelation coefficients were explored by study characteristics. Results: The median within-period intra-cluster correlation coefficient for the discrete-time decay model was 0.05 (interquartile range: 0.02-0.09) with a median cluster autocorrelation of 0.73 (interquartile range: 0.19-0.91). The within-period intra-cluster correlation coefficients were similar for the exchangeable, block-exchangeable and discrete-time decay correlation structures. Within-period intra-cluster correlation coefficients and cluster autocorrelations were found to vary with the number of participants per cluster-period, the period-length, type of cluster (primary care, secondary care, community or school) and country income status (high-income country or low- and middle-income country). The within-period intra-cluster correlation coefficients tended to decrease with increasing period-length and slightly decrease with increasing cluster-period sizes, while the cluster autocorrelations tended to move closer to 1 with increasing cluster-period size. Using the CLustered OUtcome Dataset bank, an RShiny app has been developed for determining plausible values of correlation coefficients for use in sample size calculations. Discussion: This study provides a repository of intra-cluster correlations and cluster autocorrelations for longitudinal cluster trials. This can help inform sample size calculations for future longitudinal cluster randomised trials.
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