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There is a consensus in the published literature in a number of areas, although there are many different models for structure and functions of a DMC. While uncertainty remains about some issues, it is strongly recommended that an explicit set of guidelines (Charter) is prepared for each DMC prior to the start of the trial specifying clearly how it will operate.

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In 2000, about a quarter of main RCT reports mention use of a DMC. Actual use of DMCs is likely to be somewhat greater. Reporting use of a DMC was more likely for larger and longer trials among other factors. We believe the factors affecting reported use affect actual use. It is recommended that when a DMC oversees a trial, brief details should be explicitly included in the main trial paper. Standard nomenclature for DMCs is recommended.

The UK CHIC Study provides important information on the status of individuals infected with HIV in the UK, and provides a means to study the response to HAART and to monitor changes in the clinical event and death rates that have occurred since the introduction of HAART in the UK.

Several methods for the estimation and comparison of rates of change in longitudinal studies with staggered entry and informative drop-outs have been recently proposed. For multivariate normal linear models, REML estimation is used. There are various approaches to maximizing the corresponding log-likelihood; in this paper we use a restricted iterative generalized least squares method (RIGLS) combined with a nested EM algorithm. An important statistical problem in such approaches is the estimation of the standard errors adjusted for the missing data (observed data information matrix). Louis has provided a general technique for computing the observed data information in terms of completed data quantities within the EM framework. The multiple imputation (MI) method for obtaining variances can be regarded as an alternative to this. The aim of this paper is to develop, apply and compare the Louis and a modified MI method in the setting of longitudinal studies where the source of missing data is either death or disease progression (informative) or end of the study (assumed non-informative). Longitudinal data are simultaneously modelled with the missingness process. The methods are illustrated by modelling CD4 count data from an HIV-1 clinical trial and evaluated through simulation studies. Both methods, Louis and MI, are used with Monte Carlo simulations of the missing data using the appropriate conditional distributions, the former with 100 simulations, the latter with 5 and 10. It is seen that naive SEs based on the completed data likelihood can be seriously biased. This bias was largely corrected by Louis and modified MI methods, which gave broadly similar estimates. Given the relative simplicity of the modified MI method, it may be preferable.

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