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Hoffman et al. [1] proposed an elegant resampling method for analyzing clustered binary data. The focus of their paper was to perform association tests on clustered binary data using within-cluster-resampling (WCR) method. Follmann et al. [2] extended Hoffman et al.'s procedure more generally with applicability to angular data, combining of p-values, testing of vectors of parameters, and Bayesian inference. Follmann et al. [2] termed their procedure multiple outputation because all "excess" data within each cluster is thrown out multiple times. Herein, we refer to this procedure as WCR-MO. For any statistical test to be useful for a particular design, it must be robust, have adequate power, and be easy to implement and flexible. WCR-MO can be easily extended to continuous data and is a computationally intensive but simple and highly flexible method. Considering family as a cluster, one can apply WCR to familial data in genetic studies. Using simulations, we evaluated WCR-MO's robustness for analysis of a continuous trait in terms of type I error rates in genetic research. WCR-MO performed well at the 5% α-level. However, it provided inflated type I error rates for α-levels less than 5% implying the procedure is liberal and may not be ready for application to genetic studies where α levels used are typically much less than 0.05.

Within-cluster resampling for analysis of family data: Ready for prime-time?