Model mis-specification and overestimation of the intraclass correlation coefficient in cluster randomized trials

2005 ◽  
Vol 25 (6) ◽  
pp. 957-964 ◽  
Author(s):  
Bruno Giraudeau
Keyword(s):  
Correlation Coefficient ◽  
Intraclass Correlation Coefficient ◽  
Randomized Trials ◽  
Intraclass Correlation ◽  
Cluster Randomized Trials ◽  
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.

A readily available improvement over method of moments for intra-cluster correlation estimation in the context of cluster randomized trials and fitting a GEE–type marginal model for binary outcomes

2018 ◽  
Vol 16 (1) ◽  
pp. 41-51 ◽  
Author(s):  
Philip M Westgate
Keyword(s):  
Correlation Coefficient ◽  
Randomized Trials ◽  
Estimating Equation ◽  
Generalized Estimating Equation ◽  
Cluster Randomized Trials ◽  
Coefficient Estimation ◽  
Pseudo Likelihood ◽  
Cluster Randomized ◽  
Cluster Correlation ◽  
Generalized Estimating

Background/aims Cluster randomized trials are popular in health-related research due to the need or desire to randomize clusters of subjects to different trial arms as opposed to randomizing each subject individually. As outcomes from subjects within the same cluster tend to be more alike than outcomes from subjects within other clusters, an exchangeable correlation arises that is measured via the intra-cluster correlation coefficient. Intra-cluster correlation coefficient estimation is especially important due to the increasing awareness of the need to publish such values from studies in order to help guide the design of future cluster randomized trials. Therefore, numerous methods have been proposed to accurately estimate the intra-cluster correlation coefficient, with much attention given to binary outcomes. As marginal models are often of interest, we focus on intra-cluster correlation coefficient estimation in the context of fitting such a model with binary outcomes using generalized estimating equations. Traditionally, intra-cluster correlation coefficient estimation with generalized estimating equations has been based on the method of moments, although such estimators can be negatively biased. Furthermore, alternative estimators that work well, such as the analysis of variance estimator, are not as readily applicable in the context of practical data analyses with generalized estimating equations. Therefore, in this article we assess, in terms of bias, the readily available residual pseudo-likelihood approach to intra-cluster correlation coefficient estimation with the GLIMMIX procedure of SAS (SAS Institute, Cary, NC). Furthermore, we study a possible corresponding approach to confidence interval construction for the intra-cluster correlation coefficient. Methods We utilize a simulation study and application example to assess bias in intra-cluster correlation coefficient estimates obtained from GLIMMIX using residual pseudo-likelihood. This estimator is contrasted with method of moments and analysis of variance estimators which are standards of comparison. The approach to confidence interval construction is assessed by examining coverage probabilities. Results Overall, the residual pseudo-likelihood estimator performs very well. It has considerably less bias than moment estimators, which are its competitor for general generalized estimating equation–based analyses, and therefore, it is a major improvement in practice. Furthermore, it works almost as well as analysis of variance estimators when they are applicable. Confidence intervals have near-nominal coverage when the intra-cluster correlation coefficient estimate has negligible bias. Conclusion Our results show that the residual pseudo-likelihood estimator is a good option for intra-cluster correlation coefficient estimation when conducting a generalized estimating equation–based analysis of binary outcome data arising from cluster randomized trials. The estimator is practical in that it is simply a result from fitting a marginal model with GLIMMIX, and a confidence interval can be easily obtained. An additional advantage is that, unlike most other options for performing generalized estimating equation–based analyses, GLIMMIX provides analysts the option to utilize small-sample adjustments that ensure valid inference.

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.

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