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Accounting for informative non‐compliance with a bivariate exponential model in the design of endpoint trials
Authors:Qi Jiang  Steven Snapinn  Boris Iglewicz
Abstract: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 non‐compliance, 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 © 2005 John Wiley & Sons, Ltd.
Keywords:absolutely continuous bivariate exponential distribution  Block and Basu bivariate exponential distribution  clinical trials  discontinuation  intention‐to‐treat  sample size  survival analysis
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