Intra-cluster correlations from the CLustered OUtcome Dataset bank to inform the design of longitudinal cluster trials. (October 2021)

• Record Type: Journal Article
• Authors: Korevaar, Elizabeth; Kasza, Jessica; Taljaard, Monica; Hemming, Karla; Haines, Terry; Turner, Elizabeth L; Thompson, Jennifer A; Hughes, James P; Forbes, Andrew B
• Is Part Of: Clinical trials. Volume 18:Number 5(2021)
• Page Start: 529
• Page End: 540
• Publication Date: 2021-10
• Subjects: Intra-cluster correlation coefficient -- within-period correlation -- between-period correlation -- cluster autocorrelation -- discrete-time decay -- cluster randomised trial -- sample size; 615.5072405
• DOI: 10.1177/17407745211020852
• Languages: English
• ISSNs: 1740-7745

Abstract

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.