We present an update of mim, a program for managing multiply im- puted datasets and performing inference (estimating parameters) using Rubin’s rules for combining estimates from imputed datasets. The new features of particular importance are an option for estimating the Monte Carlo error (due...

A new set of tools is described for performing analyses of an ensemble of datasets that includes multiple copies of the original data with imputations of missing values, as required for the method of multiple imputation. The tools replace those originally developed by the authors. They are based...

The method of multiple imputation (MI) is used increasingly for analyzing datasets with missing observations. Two sets of tasks are required in order to implement the method: (a) generating multiple complete datasets in which missing values have been imputed by simulating from an appropriate...

The intra-cluster correlation coefficient (ICC) of the primary outcome plays a key role in the design and analysis of cluster randomized trials (CRTs), but the precise definition of this parameter is somewhat elusive, especially in the context of non-normally distributed outcomes. In this paper,...

Stepped wedge randomised trials are increasingly popular. Here we derive the optimal design for a fixed number of periods; this does not allocate an equal number of cluster units to each treatment sequence as might otherwise have been expected.