Within-cluster resampling

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