Exposure-to-infection information is important for estimating vaccine efficacy, but it is difficult to collect and inherently prone to missingness and mismeasurement. It is therefore not feasible to collect good exposure information on all participants in a large vaccine trial. We discuss study designs which collect detailed exposure information for only a small subset of trial participants, while collecting crude exposure information on all participants, and treat estimation of vaccine efficacy in the missing data/measurement error framework. We demonstrate with the example of an HIV vaccine trial the improvements in bias and efficiency when combining the different levels of exposure information to estimate vaccine efficacy for reducing both susceptibility an infectiousness. We compare the performance of recently developed semiparametric missing data methods of Pepe and Fleming and Robins, Hsieh, and Newey.

This is joint work with Elizabeth Halloran and Ira Longini.

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