Author(s)

Mohamed Shoukri^1*, Allan Donner^2,3, Futwan Al-Mohanna¹

¹Department of Cell Biology, Research Center King Faisal Specialist Hospital & Research Center, Riyadh, KSA.
²Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada.
³Robarts Clinical Trials, Robarts Research Institute, London, Ontario, Canada.

Effect sizes are estimated from several study designs when the subjects are individually sampled. When the samples are the aggregate cluster of individuals, the within cluster correlation must be accounted for to construct correct confidence intervals, and to conduct valid statistical inference. The purpose of this article is to propose and evaluate statistical procedures for the estimation of the variance of the estimated attributable risk in parallel groups of clusters, and in a design dividing each of k clusters into two segments creating multiple sub-clusters. The estimated variance is the first order approximation and is obtained by the delta method. We apply the methodology and propose a Wald type confidence interval on the difference between two correlated attributable risks. We also construct a test on the hypothesis of equality of two correlated attributable risks. We evaluate the power of the proposed test via Monte-Carlo simulations.

Correlated Binary Responses, Effect Size, Split-Cluster Design, Correlated Attributable Risks, Confidence Intervals, Monte-Carlo Simulations

Shoukri, M. , Donner, A. and Al-Mohanna, F. (2017) Estimation of Attributable Risk from Clustered Binary Data: The Case of Cross-Sectional and Cohort Studies. Open Journal of Statistics, 7, 240-253. doi: 10.4236/ojs.2017.72019.