Stacia M DeSantis1, Ruosha Li1, Yefei Zhang1, Xueying Wang1, Sally W Vernon2, Barbara C Tilley1, Gary Koch3. 1. Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, Houston, TX, USA. 2. Department of Health Promotions and Behavioral Science, The University of Texas Health Science Center at Houston, Houston, TX, USA. 3. Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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.
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.
Authors: Barbara C Tilley; Arch G Mainous; Jordan J Elm; Elisabeth Pickelsimer; Lea H Soderstrom; Marvella E Ford; Vanessa A Diaz; Laura A Siminoff; Keith Burau; Daniel W Smith Journal: Clin Trials Date: 2012-04 Impact factor: 2.486
Authors: Barbara C Tilley; Arch G Mainous; Rossybelle P Amorrortu; M Diane McKee; Daniel W Smith; Ruosha Li; Stacia M DeSantis; Sally W Vernon; Gary Koch; Marvella E Ford; Vanessa Diaz; Jennifer Alvidrez Journal: Contemp Clin Trials Date: 2021-07-30 Impact factor: 2.261