Within-cluster resampling

Within-cluster resampling is proposed as a new method for analysing cluster ed data. Although the focus of this paper is clustered binary data, the wit hin-cluster resampling asymptotic theory is general for many types of clust ered data. Within-cluster resampling is a simple but computationally intens ive estimation method. Its main advantage over other marginal analysis meth ods, such as generalised estimating equations (Liang & Zeger, 1986; Zeger & Liang, 1986) is that it remains valid when the risk for the outcome of int erest is related to the cluster size, which we term nonignorable cluster si ze. We present theory for the asymptotic normality and provide a consistent variance estimator for the within-cluster resampling estimator. Simulation s and an example are developed that assess the finite-sample behaviour of t he new method and show that when both methods are valid its performance is similar to that of generalised estimating equations.