Abstract

Many chronic diseases put individuals at increased risk of several different types of adverse clinical events. Typically these events are combined to define composite events which are then used as the basis of treatment evaluation. A potentially more efficient approach is to conduct separate marginal assessments of the effect of treatment on each component and then to synthesize this information across each type of event. While there is considerable potential for more powerful tests of treatment effect in this setting, it is possible that dependent censoring can cause problems. This happens when the occurrence of one type of event increases the risk of withdrawal from a study and hence alters the probability of observing events of other types. The purpose of this article is to formulate a model which reflects this type of mechanism, to evaluate the effect on the asymptotic and finite sample properties of marginal estimates, and to examine the performance of estimators obtained using flexible inverse probability weighted marginal estimating equations. Data from a motivating study are used for illustration.

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