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Appropriate statistical methods for analysing partially nested randomised controlled trials with continuous outcomes: A simulation study

Lookup NU author(s): Jane Candlish, Professor Dawn Teare

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2018 The Author(s).Background: In individually randomised trials we might expect interventions delivered in groups or by care providers to result in clustering of outcomes for participants treated in the same group or by the same care provider. In partially nested randomised controlled trials (pnRCTs) this clustering only occurs in one trial arm, commonly the intervention arm. It is important to measure and account for between-cluster variability in trial design and analysis. We compare analysis approaches for pnRCTs with continuous outcomes, investigating the impact on statistical inference of cluster sizes, coding of the non-clustered arm, intracluster correlation coefficient (ICCs), and differential variance between intervention and control arm, and provide recommendations for analysis. Methods: We performed a simulation study assessing the performance of six analysis approaches for a two-arm pnRCT with a continuous outcome. These include: linear regression model; fully clustered mixed-effects model with singleton clusters in control arm; fully clustered mixed-effects model with one large cluster in control arm; fully clustered mixed-effects model with pseudo clusters in control arm; partially nested homoscedastic mixed effects model, and partially nested heteroscedastic mixed effects model. We varied the cluster size, number of clusters, ICC, and individual variance between the two trial arms. Results: All models provided unbiased intervention effect estimates. In the partially nested mixed-effects models, methods for classifying the non-clustered control arm had negligible impact. Failure to account for even small ICCs resulted in inflated Type I error rates and over-coverage of confidence intervals. Fully clustered mixed effects models provided poor control of the Type I error rates and biased ICC estimates. The heteroscedastic partially nested mixed-effects model maintained relatively good control of Type I error rates, unbiased ICC estimation, and did not noticeably reduce power even with homoscedastic individual variances across arms. Conclusions: In general, we recommend the use of a heteroscedastic partially nested mixed-effects model, which models the clustering in only one arm, for continuous outcomes similar to those generated under the scenarios of our simulations study. However, with few clusters (3-6), small cluster sizes (5-10), and small ICC (≤0.05) this model underestimates Type I error rates and there is no optimal model.


Publication metadata

Author(s): Candlish J, Teare MD, Dimairo M, Flight L, Mandefield L, Walters SJ

Publication type: Article

Publication status: Published

Journal: BMC Medical Research Methodology

Year: 2018

Volume: 18

Issue: 1

Online publication date: 11/10/2018

Acceptance date: 18/09/2018

Date deposited: 11/11/2019

ISSN (electronic): 1471-2288

Publisher: BioMed Central Ltd

URL: https://doi.org/10.1186/s12874-018-0559-x

DOI: 10.1186/s12874-018-0559-x

PubMed id: 30314463


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