scholarly journals Intracluster correlation coefficients in the Greater Mekong Subregion for sample size calculations of cluster randomized malaria trials

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Pimnara Peerawaranun ◽  
Jordi Landier ◽  
Francois H. Nosten ◽  
Thuy-Nhien Nguyen ◽  
Tran Tinh Hien ◽  
...  
Keyword(s):  
Randomized Trial ◽  
Sample Size ◽  
Randomized Trials ◽  
Cluster Randomized Trial ◽  
Cluster Randomized Trials ◽  
Intracluster Correlation ◽  
Variable Approach ◽  
Sample Size Calculations ◽  
Cluster Randomized ◽  
Greater Mekong Subregion

Abstract Background Sample size calculations for cluster randomized trials are a recognized methodological challenge for malaria research in pre-elimination settings. Positively correlated responses from the participants in the same cluster are a key feature in the estimated sample size required for a cluster randomized trial. The degree of correlation is measured by the intracluster correlation coefficient (ICC) where a higher coefficient suggests a closer correlation hence less heterogeneity within clusters but more heterogeneity between clusters. Methods Data on uPCR-detected Plasmodium falciparum and Plasmodium vivax infections from a recent cluster randomized trial which aimed at interrupting malaria transmission through mass drug administrations were used to calculate the ICCs for prevalence and incidence of Plasmodium infections. The trial was conducted in four countries in the Greater Mekong Subregion, Laos, Myanmar, Vietnam and Cambodia. Exact and simulation approaches were used to estimate ICC values for both the prevalence and the incidence of parasitaemia. In addition, the latent variable approach to estimate ICCs for the prevalence was utilized. Results The ICCs for prevalence ranged between 0.001 and 0.082 for all countries. The ICC from the combined 16 villages in the Greater Mekong Subregion were 0.26 and 0.21 for P. falciparum and P. vivax respectively. The ICCs for incidence of parasitaemia ranged between 0.002 and 0.075 for Myanmar, Cambodia and Vietnam. There were very high ICCs for incidence in the range of 0.701 to 0.806 in Laos during follow-up. Conclusion ICC estimates can help researchers when designing malaria cluster randomized trials. A high variability in ICCs and hence sample size requirements between study sites was observed. Realistic sample size estimates for cluster randomized malaria trials in the Greater Mekong Subregion have to assume high between cluster heterogeneity and ICCs. This work focused on uPCR-detected infections; there remains a need to develop more ICC references for trials designed around prevalence and incidence of clinical outcomes. Adequately powered trials are critical to estimate the benefit of interventions to malaria in a reliable and reproducible fashion. Trial registration: ClinicalTrials.govNCT01872702. Registered 7 June 2013. Retrospectively registered. https://clinicaltrials.gov/ct2/show/NCT01872702

scholarly journals A new dependence parameter approach to improve the design of cluster randomized trials with binary outcomes

2011 ◽  
Vol 8 (6) ◽  
pp. 687-698 ◽  
Author(s):  
Catherine M Crespi ◽  
Weng Kee Wong ◽  
Sheng Wu
Keyword(s):  
Sample Size ◽  
Randomized Trials ◽  
Pearson Correlation ◽  
Sample Size Determination ◽  
Binary Outcomes ◽  
Alternative Measure ◽  
Cluster Randomized Trials ◽  
Sample Size Calculations ◽  
Cluster Randomized ◽  
Measure Of Dependence

Background and Purpose Power and sample size calculations for cluster randomized trials require prediction of the degree of correlation that will be realized among outcomes of participants in the same cluster. This correlation is typically quantified as the intraclass correlation coefficient (ICC), defined as the Pearson correlation between two members of the same cluster or proportion of the total variance attributable to variance between clusters. It is widely known but perhaps not fully appreciated that for binary outcomes, the ICC is a function of outcome prevalence. Hence, the ICC and the outcome prevalence are intrinsically related, making the ICC poorly generalizable across study conditions and between studies with different outcome prevalences. Methods We use a simple parametrization of the ICC that aims to isolate that part of the ICC that measures dependence among responses within a cluster from the outcome prevalence. We incorporate this parametrization into sample size calculations for cluster randomized trials and compare our method to the traditional approach using the ICC. Results Our dependence parameter, R, may be less influenced by outcome prevalence and has an intuitive meaning that facilitates interpretation. Estimates of R from previous studies can be obtained using simple statistics. Comparison of methods showed that the traditional ICC approach to sample size determination tends to overpower studies under many scenarios, calling for more clusters than truly required. Limitations The methods are developed for equal-sized clusters, whereas cluster size may vary in practice. Conclusions The dependence parameter R is an alternative measure of dependence among binary outcomes in cluster randomized trials that has a number of advantages over the ICC.

scholarly journals Sample size calculations for the design of cluster randomized trials: A summary of methodology

2015 ◽  
Vol 42 ◽  
pp. 41-50 ◽  
Author(s):  
Fei Gao ◽  
Arul Earnest ◽  
David B. Matchar ◽  
Michael J. Campbell ◽  
David Machin
Keyword(s):  
Sample Size ◽  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Sample Size Calculations ◽  
Cluster Randomized

Sample size calculations for 3-level cluster randomized trials

2008 ◽  
Vol 5 (5) ◽  
pp. 486-495 ◽  
Author(s):  
Steven Teerenstra ◽  
Mirjam Moerbeek ◽  
Theo van Achterberg ◽  
Ben J Pelzer ◽  
George F Borm
Keyword(s):  
Sample Size ◽  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Sample Size Calculations ◽  
Cluster Randomized ◽  
Level Cluster

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.

Relative efficiency and sample size for cluster randomized trials with variable cluster sizes

2010 ◽  
Vol 8 (1) ◽  
pp. 27-36 ◽  
Author(s):  
Zhiying You ◽  
O Dale Williams ◽  
Inmaculada Aban ◽  
Edmond Kato Kabagambe ◽  
Hemant K Tiwari ◽  
...  
Keyword(s):  
Sample Size ◽  
Relative Efficiency ◽  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Variable Cluster ◽  
Cluster Randomized

Sample size estimation for modified Poisson analysis of cluster randomized trials with a binary outcome

2021 ◽  
pp. 096228022199041
Author(s):  
Fan Li ◽  
Guangyu Tong
Keyword(s):  
Relative Risk ◽  
Sample Size ◽  
Poisson Regression ◽  
Cluster Size ◽  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Binomial Regression ◽  
Size Variability ◽  
Working Correlation ◽  
Cluster Randomized

The modified Poisson regression coupled with a robust sandwich variance has become a viable alternative to log-binomial regression for estimating the marginal relative risk in cluster randomized trials. However, a corresponding sample size formula for relative risk regression via the modified Poisson model is currently not available for cluster randomized trials. Through analytical derivations, we show that there is no loss of asymptotic efficiency for estimating the marginal relative risk via the modified Poisson regression relative to the log-binomial regression. This finding holds both under the independence working correlation and under the exchangeable working correlation provided a simple modification is used to obtain the consistent intraclass correlation coefficient estimate. Therefore, the sample size formulas developed for log-binomial regression naturally apply to the modified Poisson regression in cluster randomized trials. We further extend the sample size formulas to accommodate variable cluster sizes. An extensive Monte Carlo simulation study is carried out to validate the proposed formulas. We find that the proposed formulas have satisfactory performance across a range of cluster size variability, as long as suitable finite-sample corrections are applied to the sandwich variance estimator and the number of clusters is at least 10. Our findings also suggest that the sample size estimate under the exchangeable working correlation is more robust to cluster size variability, and recommend the use of an exchangeable working correlation over an independence working correlation for both design and analysis. The proposed sample size formulas are illustrated using the Stop Colorectal Cancer (STOP CRC) trial.

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