scholarly journals Design, implementation, and analysis considerations for cluster-randomized trials in infection control and hospital epidemiology: A systematic review

2019 ◽  
Vol 40 (6) ◽  
pp. 686-692
Author(s):  
Lyndsay M. O’Hara ◽  
Natalia Blanco ◽  
Surbhi Leekha ◽  
Kristen A. Stafford ◽  
Gerard P. Slobogean ◽  
...  
Keyword(s):  
Systematic Review ◽  
Infection Control ◽  
Randomized Trials ◽  
Intraclass Correlation ◽  
Healthcare Setting ◽  
Cluster Randomized Trials ◽  
Hospital Epidemiology ◽  
Design Type ◽  
Cluster Randomized ◽  
Healthcare Epidemiology

AbstractBackground:In cluster-randomized trials (CRT), groups rather than individuals are randomized to interventions. The aim of this study was to present critical design, implementation, and analysis issues to consider when planning a CRT in the healthcare setting and to synthesize characteristics of published CRT in the field of healthcare epidemiology.Methods:A systematic review was conducted to identify CRT with infection control outcomes.Results:We identified the following 7 epidemiological principles: (1) identify design type and justify the use of CRT; (2) account for clustering when estimating sample size and report intraclass correlation coefficient (ICC)/coefficient of variation (CV); (3) obtain consent; (4) define level of inference; (5) consider matching and/or stratification; (6) minimize bias and/or contamination; and (7) account for clustering in the analysis. Among 44 included studies, the most common design was CRT with crossover (n = 15, 34%), followed by parallel CRT (n = 11, 25%) and stratified CRT (n = 7, 16%). Moreover, 22 studies (50%) offered justification for their use of CRT, and 20 studies (45%) demonstrated that they accounted for clustering at the design phase. Only 15 studies (34%) reported the ICC, CV, or design effect. Also, 15 studies (34%) obtained waivers of consent, and 7 (16%) sought consent at the cluster level. Only 17 studies (39%) matched or stratified at randomization, and 10 studies (23%) did not report efforts to mitigate bias and/or contamination. Finally, 29 studies (88%) accounted for clustering in their analyses.Conclusions:We must continue to improve the design and reporting of CRT to better evaluate the effectiveness of infection control interventions in the healthcare setting.

scholarly journals 2157. Design, Implementation, and Analysis Considerations for Cluster Randomized Trials in Infection Control and Hospital Epidemiology: A Systematic Review

2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S635-S636
Author(s):  
Lyndsay O’Hara ◽  
Natalia Blanco ◽  
Surbhi Leekha ◽  
Kristen Stafford ◽  
Gerard Slobogean ◽  
...  
Keyword(s):  
Systematic Review ◽  
Infection Control ◽  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Hospital Epidemiology ◽  
Cluster Randomized

Optimal Sample Allocation Under Unequal Costs in Cluster-Randomized Trials

2020 ◽  
Vol 45 (4) ◽  
pp. 446-474
Author(s):  
Zuchao Shen ◽  
Benjamin Kelcey
Keyword(s):  
Optimal Design ◽  
Statistical Power ◽  
Randomized Trials ◽  
Intraclass Correlation ◽  
R Package ◽  
Design Parameters ◽  
Cluster Randomized Trials ◽  
Cost Structures ◽  
Cluster Randomized ◽  
Sample Allocation

Conventional optimal design frameworks consider a narrow range of sampling cost structures that thereby constrict their capacity to identify the most powerful and efficient designs. We relax several constraints of previous optimal design frameworks by allowing for variable sampling costs in cluster-randomized trials. The proposed framework introduces additional design considerations and has the potential to identify designs with more statistical power, even when some parameters are constrained due to immutable practical concerns. The results also suggest that the gains in efficiency introduced through the expanded framework are fairly robust to misspecifications of the expanded cost structure and concomitant design parameters (e.g., intraclass correlation coefficient). The proposed framework is implemented in the R package odr.

Lessons for cluster randomized trials in the twenty-first century: a systematic review of trials in primary care

2004 ◽  
Vol 1 (1) ◽  
pp. 80-90 ◽  
Author(s):  
Sandra M Eldridge ◽  
Deborah Ashby ◽  
Gene S Feder ◽  
Alicja R Rudnicka ◽  
Obioha C Ukoumunne
Keyword(s):  
Systematic Review ◽  
Primary Care ◽  
Randomized Trials ◽  
First Century ◽  
Cluster Randomized Trials ◽  
Twenty First Century ◽  
Cluster Randomized

scholarly journals Is the R coefficient of interest in cluster randomized trials with a binary outcome?

2020 ◽  
Vol 29 (9) ◽  
pp. 2470-2480
Author(s):  
Ariane M Mbekwe Yepnang ◽  
Agnès Caille ◽  
Sandra M Eldridge ◽  
Bruno Giraudeau
Keyword(s):  
Sample Size ◽  
Simulation Study ◽  
Randomized Trials ◽  
Intraclass Correlation ◽  
Sample Size Calculation ◽  
Sample Size Determination ◽  
Binary Outcomes ◽  
Cluster Randomized Trials ◽  
Empirical Power ◽  
Cluster Randomized

In cluster randomized trials, the intraclass correlation coefficient (ICC) is classically used to measure clustering. When the outcome is binary, the ICC is known to be associated with the prevalence of the outcome. This association challenges its interpretation and can be problematic for sample size calculation. To overcome these situations, Crespi et al. extended a coefficient named R, initially proposed by Rosner for ophthalmologic data, to cluster randomized trials. Crespi et al. asserted that R may be less influenced by the outcome prevalence than is the ICC, although the authors provided only empirical data to support their assertion. They also asserted that “the traditional ICC approach to sample size determination tends to overpower studies under many scenarios, calling for more clusters than truly required”, although they did not consider empirical power. The aim of this study was to investigate whether R could indeed be considered independent of the outcome prevalence. We also considered whether sample size calculation should be better based on the R coefficient or the ICC. Considering the particular case of 2 individuals per cluster, we theoretically demonstrated that R is not symmetrical around the 0.5 prevalence value. This in itself demonstrates the dependence of R on prevalence. We also conducted a simulation study to explore the case of both fixed and variable cluster sizes greater than 2. This simulation study demonstrated that R decreases when prevalence increases from 0 to 1. Both the analytical and simulation results demonstrate that R depends on the outcome prevalence. In terms of sample size calculation, we showed that an approach based on the ICC is preferable to an approach based on the R coefficient because with the former, the empirical power is closer to the nominal one. Hence, the R coefficient does not outperform the ICC for binary outcomes because it does not offer any advantage over the ICC.

scholarly journals A Review of Assumed and Reported Intracluster Correlations in Cluster Randomized Trials

2019 ◽  
Author(s):  
Xiaoran Han ◽  
Jiaye Lin ◽  
Jinjing Xu ◽  
Maggie Wang ◽  
Benny Zee ◽  
...  
Keyword(s):  
Sample Size ◽  
Randomized Trials ◽  
Intraclass Correlation ◽  
Cluster Effect ◽  
Size Estimation ◽  
Cluster Randomized Trials ◽  
Intracluster Correlation ◽  
Eligibility Criteria ◽  
Sample Size Planning ◽  
Cluster Randomized

Abstract Background Cluster randomized trials (CRTs) are widely adopted in health and primary care research. However, the cluster effect needs to be taken into account appropriately in the design and analysis of CRTs. The objectives of this study were (i) to review the reporting of intracluster correlations in CRTs; and (ii) to evaluate whether the assumed intracluster correlation measures in sample size planning are consistent with those obtained in the analysis. Methods The Aggregate Analysis of ClinicalTrials.gov database was searched to identify CRTs registered between January 1, 2004 and March 27, 2016. The selected CRTs with accessible publications were screened according to eligibility criteria. Results Of the 281 CRTs identified, the percentage of studies accounting for cluster effect increased annually. A total of 183 studies accounted for clustering in sample size estimation, among them 43% of CRTs adopted the intraclass correlation coefficient (ICC) but the exact estimated value of ICC was provided in only 26% of the included studies. In different intervention types, there were no statistically significant differences between the assumed and reported values of ICC (all p-values >0.05). Conclusion Although the difference between the values of ICC assumed in sample size planning and that reported in the analysis was not statistically significant, deficiencies in CRTs are still common, such as low rates of considering cluster effect in sample size and reporting intracluster correlation estimates. We also suggest that researchers ought to be familiar with the properties of statistical approaches to improve the analysis of CRTs. Thus, more recommendations and guidelines such as the CONSORT statement for CRTs should be suggested to researchers.

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