scholarly journals Using structural-nested models to estimate the effect of cluster-level adherence on individual-level outcomes with a three-armed cluster-randomized trial

2013 ◽  
Vol 33 (9) ◽  
pp. 1490-1502 ◽  
Author(s):  
Babette A. Brumback ◽  
Zhulin He ◽  
Mansi Prasad ◽  
Matthew C. Freeman ◽  
Richard Rheingans
Keyword(s):  
Randomized Trial ◽  
Cluster Randomized Trial ◽  
Individual Level ◽  
Nested Models ◽  
Cluster Randomized ◽  
Structural Nested Models ◽  
Cluster Level

scholarly journals Design and analysis of a 2-year parallel follow-up of repeated ivermectin mass drug administrations for control of malaria: Small sample considerations for cluster-randomized trials with count data

2021 ◽  
pp. 174077452110285
Author(s):  
Conner L Jackson ◽  
Kathryn Colborn ◽  
Dexiang Gao ◽  
Sangeeta Rao ◽  
Hannah C Slater ◽  
...  
Keyword(s):  
Randomized Trial ◽  
Cluster Randomized Trial ◽  
Mixed Effects ◽  
Small Sample ◽  
Mixed Effects Models ◽  
Type I ◽  
Cluster Randomized ◽  
Cluster Level ◽  
Generalized Estimating

Background: Cluster-randomized trials allow for the evaluation of a community-level or group-/cluster-level intervention. For studies that require a cluster-randomized trial design to evaluate cluster-level interventions aimed at controlling vector-borne diseases, it may be difficult to assess a large number of clusters while performing the additional work needed to monitor participants, vectors, and environmental factors associated with the disease. One such example of a cluster-randomized trial with few clusters was the “efficacy and risk of harms of repeated ivermectin mass drug administrations for control of malaria” trial. Although previous work has provided recommendations for analyzing trials like repeated ivermectin mass drug administrations for control of malaria, additional evaluation of the multiple approaches for analysis is needed for study designs with count outcomes. Methods: Using a simulation study, we applied three analysis frameworks to three cluster-randomized trial designs (single-year, 2-year parallel, and 2-year crossover) in the context of a 2-year parallel follow-up of repeated ivermectin mass drug administrations for control of malaria. Mixed-effects models, generalized estimating equations, and cluster-level analyses were evaluated. Additional 2-year parallel designs with different numbers of clusters and different cluster correlations were also explored. Results: Mixed-effects models with a small sample correction and unweighted cluster-level summaries yielded both high power and control of the Type I error rate. Generalized estimating equation approaches that utilized small sample corrections controlled the Type I error rate but did not confer greater power when compared to a mixed model approach with small sample correction. The crossover design generally yielded higher power relative to the parallel equivalent. Differences in power between analysis methods became less pronounced as the number of clusters increased. The strength of within-cluster correlation impacted the relative differences in power. Conclusion: Regardless of study design, cluster-level analyses as well as individual-level analyses like mixed-effects models or generalized estimating equations with small sample size corrections can both provide reliable results in small cluster settings. For 2-year parallel follow-up of repeated ivermectin mass drug administrations for control of malaria, we recommend a mixed-effects model with a pseudo-likelihood approximation method and Kenward–Roger correction. Similarly designed studies with small sample sizes and count outcomes should consider adjustments for small sample sizes when using a mixed-effects model or generalized estimating equation for analysis. Although the 2-year parallel follow-up of repeated ivermectin mass drug administrations for control of malaria is already underway as a parallel trial, applying the simulation parameters to a crossover design yielded improved power, suggesting that crossover designs may be valuable in settings where the number of available clusters is limited. Finally, the sensitivity of the analysis approach to the strength of within-cluster correlation should be carefully considered when selecting the primary analysis for a cluster-randomized trial.

Distinguishing Between Cross- and Cluster-Level Mediation Processes in the Cluster Randomized Trial

2012 ◽  
Vol 41 (4) ◽  
pp. 630-670 ◽  
Author(s):  
Keenan A. Pituch ◽  
Laura M. Stapleton
Keyword(s):  
Randomized Trial ◽  
Cluster Randomized Trial ◽  
Cluster Randomized ◽  
Mediation Processes ◽  
Cluster Level

scholarly journals Simple compared to covariate-constrained randomization methods in balancing baseline characteristics: a case study of randomly allocating 72 hemodialysis centers in a cluster trial

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ahmed A. Al-Jaishi ◽  
Stephanie N. Dixon ◽  
Eric McArthur ◽  
P. J. Devereaux ◽  
Lehana Thabane ◽  
...  
Keyword(s):  
Principal Components ◽  
Cluster Randomized Trial ◽  
Median Number ◽  
Individual Level ◽  
Level Data ◽  
Parallel Group ◽  
Baseline Characteristics ◽  
Cluster Randomized ◽  
Cluster Level

Abstract Background and aim Some parallel-group cluster-randomized trials use covariate-constrained rather than simple randomization. This is done to increase the chance of balancing the groups on cluster- and patient-level baseline characteristics. This study assessed how well two covariate-constrained randomization methods balanced baseline characteristics compared with simple randomization. Methods We conducted a mock 3-year cluster-randomized trial, with no active intervention, that started April 1, 2014, and ended March 31, 2017. We included a total of 11,832 patients from 72 hemodialysis centers (clusters) in Ontario, Canada. We randomly allocated the 72 clusters into two groups in a 1:1 ratio on a single date using individual- and cluster-level data available until April 1, 2013. Initially, we generated 1000 allocation schemes using simple randomization. Then, as an alternative, we performed covariate-constrained randomization based on historical data from these centers. In one analysis, we restricted on a set of 11 individual-level prognostic variables; in the other, we restricted on principal components generated using 29 baseline historical variables. We created 300,000 different allocations for the covariate-constrained randomizations, and we restricted our analysis to the 30,000 best allocations based on the smallest sum of the penalized standardized differences. We then randomly sampled 1000 schemes from the 30,000 best allocations. We summarized our results with each randomization approach as the median (25th and 75th percentile) number of balanced baseline characteristics. There were 156 baseline characteristics, and a variable was balanced when the between-group standardized difference was ≤ 10%. Results The three randomization techniques had at least 125 of 156 balanced baseline characteristics in 90% of sampled allocations. The median number of balanced baseline characteristics using simple randomization was 147 (142, 150). The corresponding value for covariate-constrained randomization using 11 prognostic characteristics was 149 (146, 151), while for principal components, the value was 150 (147, 151). Conclusion In this setting with 72 clusters, constraining the randomization using historical information achieved better balance on baseline characteristics compared with simple randomization; however, the magnitude of benefit was modest.

Reducing mental-illness stigma via high school clubs: A matched-pair, cluster-randomized trial.

2020 ◽  
Vol 5 (2) ◽  
pp. 230-239
Author(s):  
Shaikh I. Ahmad ◽  
Bennett L. Leventhal ◽  
Brittany N. Nielsen ◽  
Stephen P. Hinshaw
Keyword(s):  
High School ◽  
Mental Illness ◽  
Randomized Trial ◽  
Cluster Randomized Trial ◽  
Mental Illness Stigma ◽  
Matched Pair ◽  
Cluster Randomized
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