Accounting for informative non-compliance with a bivariate exponential model in the design of endpoint trials

Failure to adjust for informative non-compliance, a common phenomenon in endpoint trials, can lead to a considerably underpowered study. However, standard methods for sample size calculation assume that non-compliance is non-informative. One existing method to account for informative noncompliance, based on a two-subpopulation model, is limited with respect to the degree of association between the risk of non-compliance and the risk of a study endpoint that can be modelled, and with respect to the maximum allowable rates of non-compliance and endpoints. In this paper, we introduce a new method that largely overcomes these limitations. This method is based on a model in which time to non-compliance and time to endpoint are assumed to follow a bivariate exponential distribution. Parameters of the distribution are obtained by equating them with the study design parameters. The impact of informative non-compliance is investigated across a wide range of conditions, and the method is illustrated by recalculating the sample size of a published clinical trial. Copyright (c) 2005 John Wiley & Sons, Ltd.