Intent-to-treat analysis of cluster randomized trials when clusters report unidentifiable outcome proportions. (December 2020)

• Record Type: Journal Article
• Authors: DeSantis, Stacia M; Li, Ruosha; Zhang, Yefei; Wang, Xueying; Vernon, Sally W; Tilley, Barbara C; Koch, Gary
• Is Part Of: Clinical trials. Volume 17:Number 6(2020)
• Page Start: 627
• Page End: 636
• Publication Date: 2020-12
• Subjects: Cluster randomized trials -- randomized controlled trials -- missing data -- clinical trials -- intent to treat; 615.5072405
• DOI: 10.1177/1740774520936668
• Languages: English
• ISSNs: 1740-7745

Abstract

Background: Cluster randomized trials are designed to evaluate interventions at the cluster or group level. When clusters are randomized but some clusters report no or non-analyzable data, intent-to-treat analysis, the gold standard for the analysis of randomized controlled trials, can be compromised. This article presents a very flexible statistical methodology for cluster randomized trials whose outcome is a cluster-level proportion (e.g. proportion from a cluster reporting an event) in the setting where clusters report non-analyzable data (which in general could be due to nonadherence, dropout, missingness, etc.). The approach is motivated by a previously published stratified randomized controlled trial called, "The Randomized Recruitment Intervention Trial (RECRUIT), " designed to examine the effectiveness of a trust-based continuous quality improvement intervention on increasing minority recruitment into clinical trials (ClinicalTrials.gov Identifier: NCT01911208). Methods: The novel approach exploits the use of generalized estimating equations for cluster-level reports, such that all clusters randomized at baseline are able to be analyzed, and intervention effects are presented as risk ratios. Simulation studies under different outcome missingness scenarios and a variety of intra-cluster correlations are conducted. A comparative analysis of the method with imputation and per protocol approaches for RECRUIT is presented. Results: Simulation results show the novel approach produces unbiased and efficient estimates of the intervention effect that maintain the nominal type I error rate. Application to RECRUIT shows similar effect sizes when compared to the imputation and per protocol approach. Conclusion: The article demonstrates that an innovative bivariate generalized estimating equations framework allows one to implement an intent-to-treat analysis to obtain risk ratios or odds ratios, for a variety of cluster randomized designs.